Systems and methods for generating alimentary instruction sets based on vibrant constitutional guidance

ABSTRACT

A method for generating an alimentary instruction set identifying a list of supplements, comprising receiving information related to a biological extraction and physiological state of a user and generating a diagnostic output based upon the information related to the biological extraction and physiological state of the user. The generating comprises identifying a condition of the user as a function of the information related to the biological extraction and physiological state of the user and a first training set. Further, the generating includes identifying a supplement related to the identified condition of the user as a function of the identified condition of the user and a second training set. Further, the method includes generating, by an alimentary instruction set generator operating on a computing device, a supplement plan as a function of the diagnostic output, said supplement plan including the supplement related to the identified condition of the user.

RELATED APPLICATION DATA

This application is a continuation of U.S. patent application Ser. No.16/375,303, filed on Apr. 4, 2019 which is hereby incorporated byreference herein in its entirety.

FIELD OF THE INVENTION

The present invention generally relates to the field of artificialintelligence. In particular, the present invention is directed tosystems and methods for generating alimentary instruction sets based onvibrant constitutional guidance using artificial intelligence.

BACKGROUND

Currently, generation of alimentary instructions by deriving prognosticdata is a process hampered by the complexity of the data involved. Assources of biological information concerning personal constitutionsbecome increasingly complex and comprehensive, effective analysis ofdata to produce practical, and practicable, instruction sets is anincreasing challenge. Existing solutions fail to account for the volumesof information to be assessed and the multivariate complexity of therequired solutions.

SUMMARY OF THE DISCLOSURE

In one aspect of the invention a method for generating an alimentaryinstruction set identifying a list of supplements is presented. Themethod can include receiving, by a diagnostic engine operating on acomputing device, information related to a biological extraction andphysiological state of a user. Further, the method can includegenerating, by the diagnostic engine operating on the computing device,a diagnostic output based upon the information related to the biologicalextraction and physiological state of the user. The generating caninclude identifying, by a machine learning module operating on thecomputing device, a condition of the user as a function of theinformation related to the biological extraction and physiological stateof the user and a first training set. This first training may include aplurality of data entries. Each first data entry of the plurality ofdata entries can include an element of physiological state data and acorrelated first prognostic label. Further, generating may also includeidentifying, by the machine learning module operating on the computingdevice, a supplement related to the identified condition of the user asa function of the identified condition of the user and a second trainingset. This second training set can include a plurality of second dataentries. Each second data entry may include a second prognostic labeland a correlated ameliorative process label. The method can also includegenerating, by an alimentary instruction set generator operating on acomputing device, a supplement plan as a function of the diagnosticoutput. Said supplement plan can include the supplement related to theidentified condition of the user.

The method can further include one or more of the following featurestaken alone or in combination. In an embodiment, the method can includetransmitting, by the computing device, a physical performanceinstruction set to a server associated with a physical performanceentity. The physical performance instruction set may identify thesupplement plan the and the physical performance entity can beconfigured to deliver said supplement plan to the user. In anembodiment, the method can also include transmitting, by the alimentaryinstruction set generator operating on the computing device, datarelated to the supplement plan to a client device associated with theuser. The data related to the supplement plan may be configured torender a visual representation of the supplement plan in a graphicaluser interface on the client device associated with the user.

In an embodiment, the method may further include, receiving, by thediagnostic engine operating on the computing device, a dietarypreference of the user. The supplement plan may be generated further asa function of the dietary preference of the user. In an embodiment, thebiological extraction can include a physical extraction from the user.In an embodiment, the method can further include generating, via a plangeneration module operating on the computing device, a comprehensiveinstruction set as a function of the diagnostic output. Saidcomprehensive instruction set can identify the user's current prognosticstatus. The comprehensive instruction set can be generated further as afunction of the information related to the biological extraction andphysiological state of the user. The supplement plan may be generatedfurther as a function of the comprehensive instruction set.

In a further aspect of the invention, a system for generating analimentary instruction set identifying a list of supplements ispresented. The system can include a computing device, a machine learningmodule operating on the computing device, and a diagnostic engineoperating on the computing device. The diagnostic engine can beconfigured to receive information related to a biological extraction andphysiological state of a user and generate a diagnostic output basedupon the information related to the biological extraction andphysiological state of the user. The generating can include identifying,by the machine learning module operating on the computing device, acondition of the user as a function of the information related to thebiological extraction and physiological state of the user and a firsttraining set. Said first training set may include a plurality of dataentries. Each first data entry of the plurality of data entries mayinclude an element of physiological state data and a correlated firstprognostic label. The generating can also include identifying, by themachine learning module operating on the computing device, a supplementrelated to the identified condition of the user as a function of theidentified condition of the user and a second training set. Said secondtraining set may include a plurality of second data entries. Each seconddata entry can include a second prognostic label and a correlatedameliorative process label. The system can also include an alimentaryinstruction set generator operating on a computing device. Thealimentary instruction set generator can be configured to generate asupplement plan as a function of the diagnostic output. Said supplementplan may include the supplement related to the identified condition ofthe user.

The system can further include one or more of the following featurestaken alone or in combination. In an embodiment, the computing devicecan be configured to transmit a physical performance instruction set toa server associated with a physical performance entity. The physicalperformance instruction set may identify the supplement plan and thephysical performance entity can be configured to deliver the supplementplan to the user. In an embodiment, wherein the alimentary instructionset generator can be further configured to transmit data related to thesupplement plan to a client device associated with the user. The datarelated to the supplement plan may be configured to render a visualrepresentation of the supplement plan in a graphical user interface onthe client device associated with the user.

In an embodiment, the diagnostic engine can be further configured toreceive a dietary preference of the user. The alimentary instruction setgenerator may be further configured to generate the supplement plan as afunction of the dietary preference of the user. In an embodiment, thebiological extraction can comprise a physical extraction from the user.In an embodiment, the system can further include a plan generationmodule operating on the computing device. The plan generation module canbe configured generate a comprehensive instruction set as a function ofthe diagnostic output. Said comprehensive instruction set can identifythe user's current prognostic status. The plan generation module can befurther configured to generate the comprehensive instruction set as afunction of the information related to the biological extraction andphysiological state of the user. The alimentary instruction setgenerator can be further configured to generate the supplement plan as afunction of the comprehensive instruction set.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of avibrant constitutional guidance network;

FIG. 2 is a block diagram illustrating an exemplary embodiment of avibrant constitutional guidance network generating an alimentaryinstruction set;

FIG. 3 is a block diagram illustrating an exemplary embodiment of adiagnostic engine;

FIG. 4 is a block diagram illustrating embodiments of data storagefacilities for use in disclosed systems and methods;

FIG. 5 is a block diagram illustrating an exemplary embodiment of abiological extraction database;

FIG. 6 is a block diagram illustrating an exemplary embodiment of anexpert knowledge database;

FIG. 7 is a block diagram illustrating an exemplary embodiment of aprognostic label database;

FIG. 8 is a block diagram illustrating an exemplary embodiment of anameliorative process label database;

FIG. 9 is a block diagram illustrating an exemplary embodiment of aprognostic label learner and associated system elements;

FIG. 10 is a block diagram illustrating an exemplary embodiment of anameliorative label learner and associated system elements;

FIG. 11 is a block diagram illustrating an exemplary embodiment of analimentary instruction label learner and associated system elements;

FIG. 12 is a block diagram illustrating an exemplary embodiment of aplan generator module and associated system elements;

FIG. 13 is a block diagram illustrating an exemplary embodiment of aprognostic label classification database;

FIG. 14 is a block diagram illustrating an exemplary embodiment of anameliorative process label classification database and associated systemelements;

FIG. 15 is a block diagram illustrating an exemplary embodiment of anarrative language database;

FIG. 16 is a block diagram illustrating an exemplary embodiment of animage database;

FIG. 17 is a block diagram illustrating an exemplary embodiment of auser database;

FIG. 18 is a block diagram illustrating an exemplary embodiment of analimentary instruction generator module and associated system elements;

FIG. 19 is a block diagram illustrating an exemplary embodiment of analimentary instruction label classification database and associatedsystem elements;

FIG. 20 is a block diagram illustrating an exemplary embodiment of anadvisory module and associated system elements;

FIG. 21 is a block diagram illustrating an exemplary embodiment of anartificial intelligence advisor and associated system elements;

FIG. 22 is a block diagram illustrating an exemplary embodiment of adefault response database;

FIG. 23 is a flow diagram illustrating an exemplary method of generatingan alimentary instruction based on vibrant constitutional guidance; and

FIG. 24 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

Systems and methods are provided for alimentary instruction setsgenerated via machine learning. The alimentary instruction sets areconfigured to interact with a plurality of applicable processes andperformances relating to users within the vibrant constitutionalnetwork. The alimentary instruction sets may automatically interact andgenerate sets of instructions receivable by one or more performances andprocesses. The one or more performances are associated with one or morephysical performance entities configured to receive one or more subsetsof data based on the alimentary instruction sets, and the physicalperformance entities are further configured to execute instructions,orders, and requests associated with the one or more subsets of data.

Systems and methods described herein provide improvements to theexecution of alimentary and/or alimentary instruction sets; wherein thealimentary instruction sets comprise a plurality of information derivedfrom one or more analyses performed on collected data associated with auser. By using a rule-based model or a machine-learned model, one ormore analyses are performed on the collected data, and outputs oftraining data are generated based on the one or more analyses on thecollected data. The outputs are used to generate instruction sets thatare used to generate the alimentary instruction sets that are configuredto automatically interact with a plurality of performances andprocesses.

Turning now to FIG. 1, a vibrant constitutional network system 100 ispresented. System 100 includes at least a server 104 which may be housedwith, may be incorporated in, or may incorporate one or more sensors ofat least a sensor. Computing device may include, be included in, and/orcommunicate with a mobile device such as a mobile telephone orsmartphone. At least a server 104 may include a single computing deviceoperating independently, or may include two or more computing deviceoperating in concert, in parallel, sequentially or the like; two or morecomputing devices may be included together in a single computing deviceor in two or more computing devices. At least a server 104 with one ormore additional devices as described below in further detail via anetwork interface device. Network interface device may be utilized forconnecting a at least a server 104 to one or more of a variety ofnetworks, and one or more devices. Examples of a network interfacedevice include, but are not limited to, a network interface card (e.g.,a mobile network interface card, a LAN card), a modem, and anycombination thereof. Examples of a network include, but are not limitedto, a wide area network (e.g., the Internet, an enterprise network), alocal area network (e.g., a network associated with an office, abuilding, a campus or other relatively small geographic space), atelephone network, a data network associated with a telephone/voiceprovider (e.g., a mobile communications provider data and/or voicenetwork), a direct connection between two computing devices, and anycombinations thereof. A network may employ a wired and/or a wirelessmode of communication. In general, any network topology may be used.Information (e.g., data, software etc.) may be communicated to and/orfrom a computer and/or a computing device. At least a server 104 mayinclude but is not limited to, for example, a at least a server 104 orcluster of computing devices in a first location and a second computingdevice or cluster of computing devices in a second location. At least aserver 104 may include one or more computing devices dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. At least a server 104 may distribute one or more computing tasksas described below across a plurality of computing devices of computingdevice, which may operate in parallel, in series, redundantly, or in anyother manner used for distribution of tasks or memory between computingdevices. At least a server 104 may be implemented using a “sharednothing” architecture in which data is cached at the worker, in anembodiment, this may enable scalability of system 100 and/or computingdevice.

Still referring to FIG. 1, system 100 includes a diagnostic engine 108operating on the at least a server 104, wherein the diagnostic engine108 configured to receive at least a biological extraction from a userand generate a diagnostic output. At least a server 104, diagnosticengine 108, and/or one or more modules operating thereon may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, at least a server104 and/or diagnostic engine 108 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. At least a server 104 and/or diagnostic engine 108 mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Referring now to FIG. 2, a vibrant constitutional network 200 withinsystem 100 is presented. Network 200 may include one or more users 202who may interact with system 100. In application, a user extraction 204is collected from user 202 and one or more analyses are performed on theuser extraction 204 in order to generate a Comprehensive instruction set206 reflecting analyses and diagnostics associated with user 202. Basedon components included within comprehensive instruction set 206, analimentary instruction set 124 is generated. In one embodiment,alimentary instruction set 124 is generated by an alimentary instructionset generator module configured to receive a plurality of alimentaryinformation relating to user 202 derived from comprehensive instructionset 206. Information contained in the Comprehensive instruction set 206may be supplemented by one or more creditable sources either within oroutside of network 200.

In one embodiment, and still viewing FIG. 2, alimentary instruction set124 is reflects an applicable solution to nourishment requirements,health deficiencies, and other applicable factors associated with thehealth, nutrition, and wellness of user 202. Alimentary instruction set124 may include performances 210-216 configured to reflect a pluralityof performances triggered by alimentary instruction set 124 within thecomprehensive instruction set 206. For example, alimentary instructionset 124 may comprise a component seeking to remedy a vitamin deficiencyof user 202 based on information within the comprehensive instructionset 206. Based on this component, alimentary instruction set 124 mayautomatically communicate with performance A 210 by transmitting aplurality of executable instructions to performance A 210 that result ina request to order vitamins or supplements to remedy the deficiency ofuser 202. In a particular embodiment of the invention, performances210-216 may be companies, facilities, organizations, platforms,programs, mobile applications, networks, or any other applicable meansconfigured to receive and process orders, requests, or instructions. Asa non-limiting example, performances 210-216 may include enlistment ofone or more applicable professionals configured to counsel, support, ormentor user 202 regarding applicable areas associated with the health,nutrition, and wellness of user 202.

In one embodiment and continuing to refer to FIG. 2, alimentaryinstruction set 124 and/or a user device associated with an alimentaryinstruction set may be presented to user 202 via a graphical userinterface, which may be configured to interact with user 202 allowinguser 202 to monitor and amend details associated with alimentaryinstruction set 124. Furthermore, an alimentary instruction set may bemonitored and amended based on input provided by an applicableprofessional either associated with alimentary instruction set 124 ordesignated by user 202 to be associated with alimentary instruction set124.

Referring now to FIG. 2, a vibrant constitutional network 200 withinsystem 100 is presented. Network 200 may include one or more users 202who may interact with system 100. In application, a user extraction 204is collected from user 202 and one or more analyses are performed on theuser extraction 204 in order to generate a Comprehensive instruction set206 reflecting analyses and diagnostics associated with user 202. Basedon components included within Comprehensive instruction set 206, analimentary instruction set 124 is generated. In one embodiment,alimentary instruction set 124 is generated by an alimentary instructionset generator module configured to receive a plurality of alimentaryinformation relating to user 202 derived from Comprehensive instructionset 206. Information contained in the Comprehensive instruction set 206may be supplemented by one or more creditable sources either within oroutside of network 200.

In one embodiment, and still viewing FIG. 2, alimentary instruction set124 is reflects an applicable solution to nourishment requirements,health deficiencies, and other applicable factors associated with thehealth, nutrition, and wellness of user 202. Alimentary instruction set124 may include performances 210-216 configured to reflect a pluralityof performances triggered by alimentary instruction set 124 within theComprehensive instruction set 206. For example, alimentary instructionset 124 may comprise a component seeking to remedy a vitamin deficiencyof user 202 based on information within the Comprehensive instructionset 206. Based on this component, alimentary instruction set 124 mayautomatically communicate with performance A 210 by transmitting aplurality of executable instructions to performance A 210 that result ina request to order vitamins or supplements to remedy the deficiency ofuser 202. In a particular embodiment of the invention, performances210-216 may be companies, facilities, organizations, platforms,programs, mobile applications, networks, or any other applicable meansconfigured to receive and process orders, requests, or instructions. Asa non-limiting example, performances 210-216 may include enlistment ofone or more applicable professionals configured to counsel, support, ormentor user 202 regarding applicable areas associated with the health,nutrition, and wellness of user 202.

In one embodiment and continuing to refer to FIG. 2, alimentaryinstruction set 124 may be presented on a graphical user interface ofuser client device 132, which may be configured to interact with user202 allowing user 202 to monitor and amend details associated withalimentary instruction set 124. Furthermore, alimentary instruction set124 may be configured to be monitored and amended by an applicableprofessional, via an alternative client device, either associated withgeneration of alimentary instruction set 124 or designated by user 202to be associated with alimentary instruction set 124.

Referring now to FIG. 3, at least a server 104 and/or diagnostic engine108 may be designed and configured to receive training data. Trainingdata, as used herein, is data containing correlation that amachine-learning process may use to model relationships between two ormore categories of data elements. For instance, and without limitation,training data may include a plurality of data entries, each entryrepresenting a set of data elements that were recorded, received, and/orgenerated together; data elements may be correlated by shared existencein a given data entry, by proximity in a given data entry, or the like.Multiple data entries in training data may evince one or more trends incorrelations between categories of data elements; for instance, andwithout limitation, a higher value of a first data element belonging toa first category of data element may tend to correlate to a higher valueof a second data element belonging to a second category of data element,indicating a possible proportional or other mathematical relationshiplinking values belonging to the two categories. Multiple categories ofdata elements may be related in training data according to variouscorrelations; correlations may indicate causative and/or predictivelinks between categories of data elements, which may be modeled asrelationships such as mathematical relationships by machine-learningprocesses as described in further detail below. Training data may beformatted and/or organized by categories of data elements, for instanceby associating data elements with one or more descriptors correspondingto categories of data elements. As a non-limiting example, training datamay include data entered in standardized forms by persons or processes,such that entry of a given data element in a given field in a form maybe mapped to one or more descriptors of categories. Elements in trainingdata may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training datamay be provided in fixed-length formats, formats linking positions ofdata to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),enabling processes or devices to detect categories of data.

Alternatively or additionally, and still referring to FIG. 3, trainingdata may include one or more elements that are not categorized; that is,training data may not be formatted or contain descriptors for someelements of data. Machine-learning algorithms and/or other processes maysort training data according to one or more categorizations using, forinstance, natural language processing algorithms, tokenization,detection of correlated values in raw data and the like; categories maybe generated using correlation and/or other processing algorithms. As anon-limiting example, in a corpus of text, phrases making up a number“n” of compound words, such as nouns modified by other nouns, may beidentified according to a statistically significant prevalence ofn-grams containing such words in a particular order; such an n-gram maybe categorized as an element of language such as a “word” to be trackedsimilarly to single words, generating a new category as a result ofstatistical analysis. Similarly, in a data entry including some textualdata, a person's name and/or a description of a medical condition ortherapy may be identified by reference to a list, dictionary, or othercompendium of terms, permitting ad-hoc categorization bymachine-learning algorithms, and/or automated association of data in thedata entry with descriptors or into a given format. The ability tocategorize data entries automatedly may enable the same training data tobe made applicable for two or more distinct machine-learning algorithmsas described in further detail below.

Still referring to FIG. 3, categorization device may be configured toreceive a first training set 300 including a plurality of first dataentries, each first data entry of the first training set 300 includingat least an element of physiological state data 304 and at least acorrelated first prognostic label 308. At least an element ofphysiological state data 304 may include any data indicative of aperson's physiological state; physiological state may be evaluated withregard to one or more measures of health of a person's body, one or moresystems within a person's body such as a circulatory system, a digestivesystem, a nervous system, or the like, one or more organs within aperson's body, and/or any other subdivision of a person's body usefulfor diagnostic or prognostic purposes. Physiological state data 304 mayinclude, without limitation, hematological data, such as red blood cellcount, which may include a total number of red blood cells in a person'sblood and/or in a blood sample, hemoglobin levels, hematocritrepresenting a percentage of blood in a person and/or sample that iscomposed of red blood cells, mean corpuscular volume, which may be anestimate of the average red blood cell size, mean corpuscularhemoglobin, which may measure average weight of hemoglobin per red bloodcell, mean corpuscular hemoglobin concentration, which may measure anaverage concentration of hemoglobin in red blood cells, platelet count,mean platelet volume which may measure the average size of platelets,red blood cell distribution width, which measures variation in red bloodcell size, absolute neutrophils, which measures the number of neutrophilwhite blood cells, absolute quantities of lymphocytes such as B-cells,T-cells, Natural Killer Cells, and the like, absolute numbers ofmonocytes including macrophage precursors, absolute numbers ofeosinophils, and/or absolute counts of basophils. Physiological statedata 304 may include, without limitation, immune function data such asInterleukine-6 (IL-6), TNF-alpha, systemic inflammatory cytokines, andthe like.

Continuing to refer to FIG. 3, physiological state data 304 may include,without limitation, data describing blood-born lipids, including totalcholesterol levels, high-density lipoprotein (HDL) cholesterol levels,low-density lipoprotein (LDL) cholesterol levels, very low-densitylipoprotein (VLDL) cholesterol levels, levels of triglycerides, and/orany other quantity of any blood-born lipid or lipid-containingsubstance. Physiological state data 304 may include measures of glucosemetabolism such as fasting glucose levels and/or hemoglobin A1-C(HbA1c)levels. Physiological state data 304 may include, without limitation,one or more measures associated with endocrine function, such as withoutlimitation, quantities of dehydroepiandrosterone (DHEAS), DHEA-Sulfate,quantities of cortisol, ratio of DHEAS to cortisol, quantities oftestosterone quantities of estrogen, quantities of growth hormone (GH),insulin-like growth factor 1 (IGF-1), quantities of adipokines such asadiponectin, leptin, and/or ghrelin, quantities of somatostatin,progesterone, or the like. Physiological state data 304 may includemeasures of estimated glomerular filtration rate (eGFR). Physiologicalstate data 304 may include quantities of C-reactive protein, estradiol,ferritin, folate, homocysteine, prostate-specific Ag,thyroid-stimulating hormone, vitamin D, 25 hydroxy, blood urea nitrogen,creatinine, sodium, potassium, chloride, carbon dioxide, uric acid,albumin, globulin, calcium, phosphorus, alkaline photophatase, alanineamino transferase, aspartate amino transferase, lactate dehydrogenase(LDH), bilirubin, gamma-glutamyl transferase (GGT), iron, and/or totaliron binding capacity (TIBC), or the like. Physiological state data 304may include antinuclear antibody levels. Physiological state data 304may include aluminum levels. Physiological state data 304 may includearsenic levels. Physiological state data 304 may include levels offibronigen, plasma cystatin C, and/or brain natriuretic peptide.

Continuing to refer to FIG. 3, physiological state data 304 may includemeasures of lung function such as forced expiratory volume, one second(FEV-1) which measures how much air can be exhaled in one secondfollowing a deep inhalation, forced vital capacity (FVC), which measuresthe volume of air that may be contained in the lungs. Physiologicalstate data 304 may include a measurement blood pressure, includingwithout limitation systolic and diastolic blood pressure. Physiologicalstate data 304 may include a measure of waist circumference.Physiological state data 304 may include body mass index (BMI).Physiological state data 304 may include one or more measures of bonemass and/or density such as dual-energy x-ray absorptiometry.Physiological state data 304 may include one or more measures of musclemass. Physiological state data 304 may include one or more measures ofphysical capability such as without limitation measures of gripstrength, evaluations of standing balance, evaluations of gait speed,pegboard tests, timed up and go tests, and/or chair rising tests.

Still viewing FIG. 3, physiological state data 304 may include one ormore measures of cognitive function, including without limitation Reyauditory verbal learning test results, California verbal learning testresults, NIH toolbox picture sequence memory test, Digital symbol codingevaluations, and/or Verbal fluency evaluations. Physiological state data304 may include one or more evaluations of sensory ability, includingmeasures of audition, vision, olfaction, gustation, vestibular functionand pain. Physiological state data 304 may include genomic data,including deoxyribonucleic acid (DNA) samples and/or sequences, such aswithout limitation DNA sequences contained in one or more chromosomes inhuman cells. Genomic data may include, without limitation, ribonucleicacid (RNA) samples and/or sequences, such as samples and/or sequences ofmessenger RNA (mRNA) or the like taken from human cells. Genetic datamay include telomere lengths. Genomic data may include epigenetic dataincluding data describing one or more states of methylation of geneticmaterial. Physiological state data 304 may include proteomic data, whichas used herein is data describing all proteins produced and/or modifiedby an organism, colony of organisms, or system of organisms, and/or asubset thereof. Physiological state data 304 may include data concerninga microbiome of a person, which as used herein includes any datadescribing any microorganism and/or combination of microorganisms livingon or within a person, including without limitation biomarkers, genomicdata, proteomic data, and/or any other metabolic or biochemical datauseful for analysis of the effect of such microorganisms on otherphysiological state data 304 of a person, and/or on prognostic labelsand/or alimentary data processes as described in further detail below.Physiological state data 304 may include any physiological state data304, as described above, describing any multicellular organism living inor on a person including any parasitic and/or symbiotic organisms livingin or on the persons; non-limiting examples may include mites,nematodes, flatworms, or the like. Examples of physiological state data304 described in this disclosure are presented for illustrative purposesonly and are not meant to be exhaustive. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousadditional examples of physiological state data 304 that may be usedconsistently with descriptions of systems and methods as provided inthis disclosure.

Continuing to refer to FIG. 3, each element of first training set 300includes at least a first prognostic label 308. A prognostic label, asdescribed herein, is an element of data identifying and/or describing acurrent, incipient, or probable future medical condition affecting aperson; medical condition may include a particular disease, one or moresymptoms associated with a syndrome, a syndrome, and/or any othermeasure of current or future health and/or healthy aging. At least aprognostic label may be associated with a physical and/or somaticcondition, a mental condition such as a mental illness, neurosis, or thelike, or any other condition affecting human health that may beassociated with one or more elements of physiological state data 304 asdescribed in further detail below. Conditions associated with prognosticlabels may include, without limitation one or more diseases, defined forpurposes herein as conditions that negatively affect structure and/orfunction of part or all of an organism. Conditions associated withprognostic labels may include, without limitation, acute or chronicinfections, including without limitation infections by bacteria,archaea, viruses, viroids, prions, single-celled eukaryotic organismssuch as amoeba, paramecia, trypanosomes, plasmodia, leishmania, and/orfungi, and/or multicellular parasites such as nematodes, arthropods,fungi, or the like. Prognostic labels may be associated with one or moreimmune disorders, including without limitation immunodeficiencies and/orauto-immune conditions. Prognostic labels may be associated with one ormore metabolic disorders. Prognostic labels may be associated with oneor more endocrinal disorders. Prognostic labels may be associated withone or more cardiovascular disorders. Prognostic labels may beassociated with one or more respiratory disorders. Prognostic labels maybe associated with one or more disorders affecting connective tissue.Prognostic labels may be associated with one or more digestivedisorders. Prognostic labels may be associated with one or moreneurological disorders such as neuromuscular disorders, dementia, or thelike. Prognostic labels may be associated with one or more disorders ofthe excretory system, including without limitation nephrologicaldisorders. Prognostic labels may be associated with one or more liverdisorders. Prognostic labels may be associated with one or moredisorders of the bones such as osteoporosis. Prognostic labels may beassociated with one or more disorders affecting joints, such asosteoarthritis, gout, and/or rheumatoid arthritis. Prognostic labels beassociated with one or more cancers, including without limitationcarcinomas, lymphomas, leukemias, germ cell tumor cancers, blastomas,and/or sarcomas. Prognostic labels may include descriptors of latent,dormant, and/or apparent disorders, diseases, and/or conditions.Prognostic labels may include descriptors of conditions for which aperson may have a higher than average probability of development, suchas a condition for which a person may have a “risk factor”; forinstance, a person currently suffering from abdominal obesity may have ahigher than average probability of developing type II diabetes. Theabove-described examples are presented for illustrative purposes onlyand are not intended to be exhaustive. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousadditional examples of conditions that may be associated with prognosticlabels as described in this disclosure.

Still referring to FIG. 3, at least a prognostic label may be stored inany suitable data and/or data type. For instance, and withoutlimitation, at least a prognostic label may include textual data, suchas numerical, character, and/or string data. Textual data may include astandardized name and/or code for a disease, disorder, or the like;codes may include diagnostic codes and/or diagnosis codes, which mayinclude without limitation codes used in diagnosis classificationsystems such as The International Statistical Classification of Diseasesand Related Health Problems (ICD). In general, there is no limitation onforms textual data or non-textual data used as at least a prognosticlabel may take; persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various forms which may be suitablefor use as at least a prognostic label consistently with thisdisclosure.

Still viewing FIG. 3, physiological state data 304 may include one ormore measures of cognitive function, including without limitation Reyauditory verbal learning test results, California verbal learning testresults, NIH toolbox picture sequence memory test, Digital symbol codingevaluations, and/or Verbal fluency evaluations. Physiological state data204 may include one or more measures of psychological function or state,such as without limitation clinical interviews, assessments ofintellectual functioning and/or intelligence quotient (IQ) tests,personality assessments, and/or behavioral assessments. Physiologicalstate data 204 may include one or more psychological self-assessments,which may include any self-administered and/or automatedlycomputer-administered assessments, whether administered within system100 and/or via a third-party service or platform.

With continued reference to FIG. 4, in each first data element of firsttraining set 300, at least a first prognostic label 308 of the dataelement is correlated with at least an element of physiological statedata 304 of the data element. In an embodiment, an element ofphysiological data is correlated with a prognostic label where theelement of physiological data is located in the same data element and/orportion of data element as the prognostic label; for example, andwithout limitation, an element of physiological data is correlated witha prognostic element where both element of physiological data andprognostic element are contained within the same first data element ofthe first training set 300. As a further example, an element ofphysiological data is correlated with a prognostic element where bothshare a category label as described in further detail below, where eachis within a certain distance of the other within an ordered collectionof data in data element, or the like. Still further, an element ofphysiological data may be correlated with a prognostic label where theelement of physiological data and the prognostic label share an origin,such as being data that was collected with regard to a single person orthe like. In an embodiment, a first datum may be more closely correlatedwith a second datum in the same data element than with a third datumcontained in the same data element; for instance, the first element andthe second element may be closer to each other in an ordered set of datathan either is to the third element, the first element and secondelement may be contained in the same subdivision and/or section of datawhile the third element is in a different subdivision and/or section ofdata, or the like. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various forms and/ordegrees of correlation between physiological data and prognostic labelsthat may exist in first training set 300 and/or first data elementconsistently with this disclosure.

In an embodiment, and still referring to FIG. 2, diagnostic engine 108may be designed and configured to associate at least an element ofphysiological state data 304 with at least a category from a list ofsignificant categories of physiological state data 304. Significantcategories of physiological state data 304 may include labels and/ordescriptors describing types of physiological state data 304 that areidentified as being of high relevance in identifying prognostic labels.As a non-limiting example, one or more categories may identifysignificant categories of physiological state data 304 based on degreeof diagnostic relevance to one or more impactful conditions and/orwithin one or more medical or public health fields. For instance, andwithout limitation, a particular set of biomarkers, test results, and/orbiochemical information may be recognized in a given medical field asuseful for identifying various disease conditions or prognoses within arelevant field. As a non-limiting example, and without limitation,physiological data describing red blood cells, such as red blood cellcount, hemoglobin levels, hematocrit, mean corpuscular volume, meancorpuscular hemoglobin, and/or mean corpuscular hemoglobin concentrationmay be recognized as useful for identifying various conditions such asdehydration, high testosterone, nutrient deficiencies, kidneydysfunction, chronic inflammation, anemia, and/or blood loss. As anadditional example, hemoglobin levels may be useful for identifyingelevated testosterone, poor oxygen deliverability, thiamin deficiency,insulin resistance, anemia, liver disease, hypothyroidism, argininedeficiency, protein deficiency, inflammation, and/or nutrientdeficiencies. In a further non-limiting example, hematocrit may beuseful for identifying dehydration, elevated testosterone, poor oxygendeliverability, thiamin deficiency, insulin resistance, anemia, liverdisease, hypothyroidism, arginine deficiency, protein deficiency,inflammation, and/or nutrient deficiencies. Similarly, measures of lipidlevels in blood, such as total cholesterol, HDL, LDL, VLDL,triglycerides, LDL-C and/or HDL-C may be recognized as useful inidentifying conditions such as poor thyroid function, insulinresistance, blood glucose dysregulation, magnesium deficiency,dehydration, kidney disease, familial hypercholesterolemia, liverdysfunction, oxidative stress, inflammation, malabsorption, anemia,alcohol abuse, diabetes, hypercholesterolemia, coronary artery disease,atherosclerosis, or the like. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalcategories of physiological data that may be used consistently with thisdisclosure.

Still referring to FIG. 1, diagnostic engine 108 may receive the list ofsignificant categories according to any suitable process; for instance,and without limitation, diagnostic engine 108 may receive the list ofsignificant categories from at least an expert. In an embodiment,diagnostic engine 108 and/or a user device connected to diagnosticengine 108 may provide a graphical user interface, which may includewithout limitation a form or other graphical element having data entryfields, wherein one or more experts, including without limitationclinical and/or scientific experts, may enter information describing oneor more categories of physiological data that the experts consider to besignificant or useful for detection of conditions; fields in graphicaluser interface may provide options describing previously identifiedcategories, which may include a comprehensive or near-comprehensive listof types of physiological data detectable using known or recordedtesting methods, for instance in “drop-down” lists, where experts may beable to select one or more entries to indicate their usefulness and/orsignificance in the opinion of the experts. Fields may include free-formentry fields such as text-entry fields where an expert may be able totype or otherwise enter text, enabling expert to propose or suggestcategories not currently recorded. Graphical user interface or the likemay include fields corresponding to prognostic labels, where experts mayenter data describing prognostic labels and/or categories of prognosticlabels the experts consider related to entered categories ofphysiological data; for instance, such fields may include drop-downlists or other pre-populated data entry fields listing currentlyrecorded prognostic labels, and which may be comprehensive, permittingeach expert to select a prognostic label and/or a plurality ofprognostic labels the expert believes to be predicted and/or associatedwith each category of physiological data selected by the expert. Fieldsfor entry of prognostic labels and/or categories of prognostic labelsmay include free-form data entry fields such as text entry fields; asdescribed above, examiners may enter data not presented in pre-populateddata fields in the free-form data entry fields. Alternatively oradditionally, fields for entry of prognostic labels may enable an expertto select and/or enter information describing or linked to a category ofprognostic label that the expert considers significant, wheresignificance may indicate likely impact on longevity, mortality, qualityof life, or the like as described in further detail below. Graphicaluser interface may provide an expert with a field in which to indicate areference to a document describing significant categories ofphysiological data, relationships of such categories to prognosticlabels, and/or significant categories of prognostic labels. Any datadescribed above may alternatively or additionally be received fromexperts similarly organized in paper form, which may be captured andentered into data in a similar way, or in a textual form such as aportable document file (PDF) with examiner entries, or the like.

With continued reference to FIG. 3, data describing significantcategories of physiological data, relationships of such categories toprognostic labels, and/or significant categories of prognostic labelsmay alternatively or additionally be extracted from one or moredocuments using a language processing module 316. Language processingmodule 316 may include any hardware and/or software module. Languageprocessing module 316 may be configured to extract, from the one or moredocuments, one or more words. One or more words may include, withoutlimitation, strings of one or characters, including without limitationany sequence or sequences of letters, numbers, punctuation, diacriticmarks, engineering symbols, geometric dimensioning and tolerancing(GD&T) symbols, chemical symbols and formulas, spaces, whitespace, andother symbols, including any symbols usable as textual data as describedabove. Textual data may be parsed into tokens, which may include asimple word (sequence of letters separated by whitespace) or moregenerally a sequence of characters as described previously. The term“token,” as used herein, refers to any smaller, individual groupings oftext from a larger source of text; tokens may be broken up by word, pairof words, sentence, or other delimitation. These tokens may in turn beparsed in various ways. Textual data may be parsed into words orsequences of words, which may be considered words as well. Textual datamay be parsed into “n-grams”, where all sequences of n consecutivecharacters are considered. Any or all possible sequences of tokens orwords may be stored as “chains”, for example for use as a Markov chainor Hidden Markov Model.

Still referring to FIG. 3, language processing module 316 may compareextracted words to categories of physiological data recorded atdiagnostic engine 108, one or more prognostic labels recorded atdiagnostic engine 108, and/or one or more categories of prognosticlabels recorded at diagnostic engine 108; such data for comparison maybe entered on diagnostic engine 108 as described above using expert datainputs or the like. In an embodiment, one or more categories may beenumerated, to find total count of mentions in such documents.Alternatively or additionally, language processing module 316 mayoperate to produce a language processing model. Language processingmodel may include a program automatically generated by diagnostic engine108 and/or language processing module 316 to produce associationsbetween one or more words extracted from at least a document and detectassociations, including without limitation mathematical associations,between such words, and/or associations of extracted words withcategories of physiological data, relationships of such categories toprognostic labels, and/or categories of prognostic labels. Associationsbetween language elements, where language elements include for purposesherein extracted words, categories of physiological data, relationshipsof such categories to prognostic labels, and/or categories of prognosticlabels may include, without limitation, mathematical associations,including without limitation statistical correlations between anylanguage element and any other language element and/or languageelements. Statistical correlations and/or mathematical associations mayinclude probabilistic formulas or relationships indicating, forinstance, a likelihood that a given extracted word indicates a givencategory of physiological data, a given relationship of such categoriesto prognostic labels, and/or a given category of prognostic labels. As afurther example, statistical correlations and/or mathematicalassociations may include probabilistic formulas or relationshipsindicating a positive and/or negative association between at least anextracted word and/or a given category of physiological data, a givenrelationship of such categories to prognostic labels, and/or a givencategory of prognostic labels; positive or negative indication mayinclude an indication that a given document is or is not indicating acategory of physiological data, relationship of such category toprognostic labels, and/or category of prognostic labels is or is notsignificant. For instance, and without limitation, a negative indicationmay be determined from a phrase such as “telomere length was not foundto be an accurate predictor of overall longevity,” whereas a positiveindication may be determined from a phrase such as “telomere length wasfound to be an accurate predictor of dementia,” as an illustrativeexample; whether a phrase, sentence, word, or other textual element in adocument or corpus of documents constitutes a positive or negativeindicator may be determined, in an embodiment, by mathematicalassociations between detected words, comparisons to phrases and/or wordsindicating positive and/or negative indicators that are stored in memoryat diagnostic engine 108, or the like.

Still referring to FIG. 3, language processing module 316 and/ordiagnostic engine 108 may generate the language processing model by anysuitable method, including without limitation a natural languageprocessing classification algorithm; language processing model mayinclude a natural language process classification model that enumeratesand/or derives statistical relationships between input term and outputterms. Algorithm to generate language processing model may include astochastic gradient descent algorithm, which may include a method thatiteratively optimizes an objective function, such as an objectivefunction representing a statistical estimation of relationships betweenterms, including relationships between input terms and output terms, inthe form of a sum of relationships to be estimated. In an alternative oradditional approach, sequential tokens may be modeled as chains, servingas the observations in a Hidden Markov Model (HMM). HMMs as used hereinare statistical models with inference algorithms that that may beapplied to the models. In such models, a hidden state to be estimatedmay include an association between an extracted word category ofphysiological data, a given relationship of such categories toprognostic labels, and/or a given category of prognostic labels. Theremay be a finite number of category of physiological data, a givenrelationship of such categories to prognostic labels, and/or a givencategory of prognostic labels to which an extracted word may pertain; anHMIM inference algorithm, such as the forward-backward algorithm or theViterbi algorithm, may be used to estimate the most likely discretestate given a word or sequence of words. Language processing module 316may combine two or more approaches. For instance, and withoutlimitation, machine-learning program may use a combination ofNaive-Bayes (NB), Stochastic Gradient Descent (SGD), and parametergrid-searching classification techniques; the result may include aclassification algorithm that returns ranked associations.

Continuing to refer to FIG. 3, generating language processing model mayinclude generating a vector space, which may be a collection of vectors,defined as a set of mathematical objects that can be added togetherunder an operation of addition following properties of associativity,commutativity, existence of an identity element, and existence of aninverse element for each vector, and can be multiplied by scalar valuesunder an operation of scalar multiplication compatible with fieldmultiplication, and that has an identity element is distributive withrespect to vector addition, and is distributive with respect to fieldaddition. Each vector in an n-dimensional vector space may berepresented by an n-tuple of numerical values. Each unique extractedword and/or language element as described above may be represented by avector of the vector space. In an embodiment, each unique extractedand/or other language element may be represented by a dimension ofvector space; as a non-limiting example, each element of a vector mayinclude a number representing an enumeration of co-occurrences of theword and/or language element represented by the vector with another wordand/or language element. Vectors may be normalized, scaled according torelative frequencies of appearance and/or file sizes. In an embodimentassociating language elements to one another as described above mayinclude computing a degree of vector similarity between a vectorrepresenting each language element and a vector representing anotherlanguage element; vector similarity may be measured according to anynorm for proximity and/or similarity of two vectors, including withoutlimitation cosine similarity, which measures the similarity of twovectors by evaluating the cosine of the angle between the vectors, whichcan be computed using a dot product of the two vectors divided by thelengths of the two vectors. Degree of similarity may include any othergeometric measure of distance between vectors.

Still referring to FIG. 3, language processing module 316 may use acorpus of documents to generate associations between language elementsin a language processing module 416, and diagnostic engine 108 may thenuse such associations to analyze words extracted from one or moredocuments and determine that the one or more documents indicatesignificance of a category of physiological data, a given relationshipof such categories to prognostic labels, and/or a given category ofprognostic labels. In an embodiment, diagnostic engine 108 may performthis analysis using a selected set of significant documents, such asdocuments identified by one or more experts as representing goodscience, good clinical analysis, or the like; experts may identify orenter such documents via graphical user interface as described or maycommunicate identities of significant documents according to any othersuitable method of electronic communication, or by providing suchidentity to other persons who may enter such identifications intodiagnostic engine 108. Documents may be entered into diagnostic engine108 by being uploaded by an expert or other persons using, withoutlimitation, file transfer protocol (FTP) or other suitable methods fortransmission and/or upload of documents; alternatively or additionally,where a document is identified by a citation, a uniform resourceidentifier (URI), uniform resource locator (URL) or other datumpermitting unambiguous identification of the document, diagnostic engine108 may automatically obtain the document using such an identifier, forinstance by submitting a request to a database or compendium ofdocuments such as JSTOR as provided by Ithaka Harbors, Inc. of New York.

Continuing to refer to FIG. 3, whether an entry indicating significanceof a category of physiological data, a given relationship of suchcategories to prognostic labels, and/or a given category of prognosticlabels is entered via graphical user interface, alternative submissionmeans, and/or extracted from a document or body of documents asdescribed above, an entry or entries may be aggregated to indicate anoverall degree of significance. For instance, each category ofphysiological data, relationship of such categories to prognosticlabels, and/or category of prognostic labels may be given an overallsignificance score; overall significance score may, for instance, beincremented each time an expert submission and/or paper indicatessignificance as described above. Persons skilled in the art, uponreviewing the entirety of this disclosure will be aware of other ways inwhich scores may be generated using a plurality of entries, includingaveraging, weighted averaging, normalization, and the like. Significancescores may be ranked; that is, all categories of physiological data,relationships of such categories to prognostic labels, and/or categoriesof prognostic labels may be ranked according significance scores, forinstance by ranking categories of physiological data, relationships ofsuch categories to prognostic labels, and/or categories of prognosticlabels higher according to higher significance scores and loweraccording to lower significance scores. Categories of physiologicaldata, relationships of such categories to prognostic labels, and/orcategories of prognostic labels may be eliminated from current use ifthey fail a threshold comparison, which may include a comparison ofsignificance score to a threshold number, a requirement thatsignificance score belong to a given portion of ranking such as athreshold percentile, quartile, or number of top-ranked scores.Significance scores may be used to filter outputs as described infurther detail below; for instance, where a number of outputs aregenerated and automated selection of a smaller number of outputs isdesired, outputs corresponding to higher significance scores may beidentified as more probable and/or selected for presentation while otheroutputs corresponding to lower significance scores may be eliminated.Alternatively or additionally, significance scores may be calculated persample type; for instance, entries by experts, documents, and/ordescriptions of purposes of a given type of physiological test or samplecollection as described above may indicate that for that type ofphysiological test or sample collection a first category ofphysiological data, relationship of such category to prognostic labels,and/or category of prognostic labels is significant with regard to thattest, while a second category of physiological data, relationship ofsuch category to prognostic labels, and/or category of prognostic labelsis not significant; such indications may be used to perform asignificance score for each category of physiological data, relationshipof such category to prognostic labels, and/or category of prognosticlabels is or is not significant per type of biological extraction, whichthen may be subjected to ranking, comparison to thresholds and/orelimination as described above.

Still referring to FIG. 3, diagnostic engine 108 may detect furthersignificant categories of physiological data, relationships of suchcategories to prognostic labels, and/or categories of prognostic labelsusing machine-learning processes, including without limitationunsupervised machine-learning processes as described in further detailbelow; such newly identified categories, as well as categories enteredby experts in free-form fields as described above, may be added topre-populated lists of categories, lists used to identify languageelements for language learning module, and/or lists used to identifyand/or score categories detected in documents, as described above.

Continuing to refer to FIG. 3, in an embodiment, diagnostic engine 108may be configured, for instance as part of receiving the first trainingset 300, to associate at least correlated first prognostic label 308with at least a category from a list of significant categories ofprognostic labels. Significant categories of prognostic labels may beacquired, determined, and/or ranked as described above. As anon-limiting example, prognostic labels may be organized according torelevance to and/or association with a list of significant conditions. Alist of significant conditions may include, without limitation,conditions having generally acknowledged impact on longevity and/orquality of life; this may be determined, as a non-limiting example, by aproduct of relative frequency of a condition within the population withyears of life and/or years of able-bodied existence lost, on average, asa result of the condition. A list of conditions may be modified for agiven person to reflect a family history of the person; for instance, aperson with a significant family history of a particular condition orset of conditions, or a genetic profile having a similarly significantassociation therewith, may have a higher probability of developing suchconditions than a typical person from the general population, and as aresult diagnostic engine 108 may modify list of significant categoriesto reflect this difference.

Still referring to FIG. 3, diagnostic engine 108 is designed andconfigured to receive a second training set 320 including a plurality ofsecond data entries. Each second data entry of the second training set320 includes at least a second prognostic label 324; at least a secondprognostic label 324 may include any label suitable for use as at leasta first prognostic label 308 as described above. Each second data entryof the second training set 320 includes at least an ameliorative processlabel 328 correlated with the at least a second prognostic label 324,where correlation may include any correlation suitable for correlationof at least a first prognostic label 308 to at least an element ofphysiological data as described above. As used herein, an ameliorativeprocess label 328 is an identifier, which may include any form ofidentifier suitable for use as a prognostic label as described above,identifying a process that tends to improve a physical condition of auser, where a physical condition of a user may include, withoutlimitation, any physical condition identifiable using a prognosticlabel. Ameliorative processes may include, without limitation, exerciseprograms, including amount, intensity, and/or types of exerciserecommended. Ameliorative processes may include, without limitation,dietary or alimentary recommendations based on data including alimentarycontent, digestibility, or the like. Ameliorative processes may includeone or more medical procedures. Ameliorative processes may include oneor more physical, psychological, or other therapies. Ameliorativeprocesses may include one or more medications. Alimentary processes maybe a form of an ameliorative process and an ameliorative output mayinclude an alimentary process. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousprocesses that may be used as ameliorative processes consistently withthis disclosure.

Continuing to refer to FIG. 3, in an embodiment diagnostic engine 108may be configured, for instance as part of receiving second training set320, to associate the at least second prognostic label 324 with at leasta category from a list of significant categories of prognostic labels.This may be performed as described above for use of lists of significantcategories with regard to at least a first prognostic label 308.Significance may be determined, and/or association with at least acategory, may be performed for prognostic labels in first training set300 according to a first process as described above and for prognosticlabels in second training set 320 according to a second process asdescribed above.

Still referring to FIG. 3, diagnostic engine 108 may be configured, forinstance as part of receiving second training set 320, to associate atleast a correlated ameliorative process label 328 with at least acategory from a list of significant categories of ameliorative processlabels 328. In an embodiment, diagnostic engine 108 and/or a user deviceconnected to diagnostic engine 108 may provide a second graphical userinterface 332 which may include without limitation a form or othergraphical element having data entry fields, wherein one or more experts,including without limitation clinical and/or scientific experts, mayenter information describing one or more categories of prognostic labelsthat the experts consider to be significant as described above; fieldsin graphical user interface may provide options describing previouslyidentified categories, which may include a comprehensive ornear-comprehensive list of types of prognostic labels, for instance in“drop-down” lists, where experts may be able to select one or moreentries to indicate their usefulness and/or significance in the opinionof the experts. Fields may include free-form entry fields such astext-entry fields where an expert may be able to type or otherwise entertext, enabling expert to propose or suggest categories not currentlyrecorded. Graphical user interface or the like may include fieldscorresponding to ameliorative labels, where experts may enter datadescribing ameliorative labels and/or categories of ameliorative labelsthe experts consider related to entered categories of prognostic labels;for instance, such fields may include drop-down lists or otherpre-populated data entry fields listing currently recorded ameliorativelabels, and which may be comprehensive, permitting each expert to selectan ameliorative label and/or a plurality of ameliorative labels theexpert believes to be predicted and/or associated with each category ofprognostic labels selected by the expert. Fields for entry ofameliorative labels and/or categories of ameliorative labels may includefree-form data entry fields such as text entry fields; as describedabove, examiners may enter data not presented in pre-populated datafields in the free-form data entry fields. Alternatively oradditionally, fields for entry of ameliorative labels may enable anexpert to select and/or enter information describing or linked to acategory of ameliorative label that the expert considers significant,where significance may indicate likely impact on longevity, mortality,quality of life, or the like as described in further detail below.Graphical user interface may provide an expert with a field in which toindicate a reference to a document describing significant categories ofprognostic labels, relationships of such categories to ameliorativelabels, and/or significant categories of ameliorative labels. Suchinformation may alternatively be entered according to any other suitablemeans for entry of expert data as described above. Data concerningsignificant categories of prognostic labels, relationships of suchcategories to ameliorative labels, and/or significant categories ofameliorative labels may be entered using analysis of documents usinglanguage processing module 316 or the like as described above. In oneembodiment, alimentary instruction set 124 may be modified to eliminatecategories of ameliorative process labels 328 that are non-alimentary.

Still referring to FIG. 3, diagnostic engine 108 may be configured, forinstance as part of receiving second training set 320, to associate atleast ameliorative process label 328 with at least a category from alist of significant categories of ameliorative process label 328. In anembodiment, diagnostic engine 108 and/or a user device connected todiagnostic engine 108 may provide a second graphical user interface 332which may include without limitation a form or other graphical elementhaving data entry fields, wherein one or more experts, including withoutlimitation clinical and/or scientific experts, may enter informationdescribing one or more categories of prognostic labels that the expertsconsider to be significant as described above; fields in graphical userinterface may provide options describing previously identifiedcategories, which may include a comprehensive or near-comprehensive listof types of prognostic labels, for instance in “drop-down” lists, whereexperts may be able to select one or more entries to indicate theirusefulness and/or significance in the opinion of the experts. Fields mayinclude free-form entry fields such as text-entry fields where an expertmay be able to type or otherwise enter text, enabling expert to proposeor suggest categories not currently recorded. Graphical user interfaceor the like may include fields corresponding to alimentary instructionset labels, where experts may enter data describing alimentaryinstruction set labels and/or categories of alimentary instruction setlabels the experts consider related to entered categories of prognosticlabels; for instance, such fields may include drop-down lists or otherpre-populated data entry fields listing currently recorded alimentaryinstruction set labels, and which may be comprehensive, permitting eachexpert to select an alimentary instruction set label and/or a pluralityof alimentary instruction set labels the expert believes to be predictedand/or associated with each category of prognostic labels selected bythe expert. Fields for entry of alimentary instruction set labels and/orcategories of alimentary instruction set labels may include free-formdata entry fields such as text entry fields; as described above,examiners may enter data not presented in pre-populated data fields inthe free-form data entry fields. Alternatively or additionally, fieldsfor entry of alimentary instruction set labels may enable an expert toselect and/or enter information describing or linked to a category ofalimentary instruction set label that the expert considers significant,where significance may indicate likely impact on longevity, mortality,quality of life, or the like as described in further detail below.Graphical user interface may provide an expert with a field in which toindicate a reference to a document describing significant categories ofprognostic labels, relationships of such categories to alimentaryinstruction set labels, and/or significant categories of alimentaryinstruction set labels. Such information may alternatively be enteredaccording to any other suitable means for entry of expert data asdescribed above. Data concerning significant categories of prognosticlabels, relationships of such categories to alimentary instruction setlabels, and/or significant categories of alimentary instruction setlabels may be entered using analysis of documents using languageprocessing module 316 or the like as described above.

In an embodiment, and still referring to FIG. 3, diagnostic engine 108may extract at least a second data entry from one or more documents;extraction may be performed using any language processing method asdescribed above. Diagnostic engine 108 may be configured, for instanceas part of receiving second training set 320, to receive at least adocument describing at least a medical history and extract at least asecond data entry of plurality of second data entries from the at leasta document. A medical history document may include, for instance, adocument received from an expert and/or medical practitioner describingtreatment of a patient; document may be anonymized by removal of one ormore patient-identifying features from document. A medical historydocument may include a case study, such as a case study published in amedical journal or written up by an expert. A medical history documentmay contain data describing and/or described by a prognostic label; forinstance, the medical history document may list a diagnosis that amedical practitioner made concerning the patient, a finding that thepatient is at risk for a given condition and/or evinces some precursorstate for the condition, or the like. A medical history document maycontain data describing and/or described by an ameliorative processlabel 328; for instance, the medical history document may list atherapy, recommendation, or other alimentary instruction set processthat a medical practitioner described or recommended to a patient. Amedical history document may describe an outcome; for instance, medicalhistory document may describe an improvement in a condition describingor described by a prognostic label, and/or may describe that thecondition did not improve. Prognostic labels, ameliorative process label328, and/or efficacy of ameliorative process label 328 may be extractedfrom and/or determined from one or more medical history documents usingany processes for language processing as described above; for instance,language processing module 316 may perform such processes. As anon-limiting example, positive and/or negative indications regardingalimentary instruction set processes identified in medical historydocuments may be determined in a manner described above fordetermination of positive and/or negative indications regardingcategories of physiological data, relationships of such categories toprognostic labels, and/or categories of prognostic labels.

With continued reference to FIG. 3, diagnostic engine 108 may beconfigured, for instance as part of receiving second training set 320,to receiving at least a second data entry of the plurality of seconddata entries from at least an expert. This may be performed, withoutlimitation using second graphical user interface 332 as described above.

Referring now to FIG. 4, data incorporated in first training set 300and/or second training set 320 may be incorporated in one or moredatabases. As a non-limiting example, one or elements of physiologicalstate data may be stored in and/or retrieved from a biologicalextraction database 400. A biological extraction database 400 mayinclude any data structure for ordered storage and retrieval of data,which may be implemented as a hardware or software module. A biologicalextraction database 400 may be implemented, without limitation, as arelational database, a key-value retrieval datastore such as a NOSQLdatabase, or any other format or structure for use as a datastore that aperson skilled in the art would recognize as suitable upon review of theentirety of this disclosure. A biological extraction database 400 mayinclude a plurality of data entries and/or records corresponding toelements of physiological data as described above. Data entries and/orrecords may describe, without limitation, data concerning particularbiological extractions that have been collected; entries may describereasons for collection of samples, such as without limitation one ormore conditions being tested for, which may be listed with relatedprognostic labels. Data entries may include prognostic labels and/orother descriptive entries describing results of evaluation of pastbiological extractions, including diagnoses that were associated withsuch samples, prognoses and/or conclusions regarding likelihood offuture diagnoses that were associated with such samples, and/or othermedical or diagnostic conclusions that were derived. Such conclusionsmay have been generated by diagnostic engine 108 in previous iterationsof methods, with or without validation of correctness by medicalprofessionals. Data entries in a biological extraction database 400 maybe flagged with or linked to one or more additional elements ofinformation, which may be reflected in data entry cells and/or in linkedtables such as tables related by one or more indices in a relationaldatabase; one or more additional elements of information may includedata associating a biological extraction and/or a person from whom abiological extraction was extracted or received with one or morecohorts, including demographic groupings such as ethnicity, sex, age,income, geographical region, or the like, one or more common diagnosesor physiological attributes shared with other persons having biologicalextractions reflected in other data entries, or the like. Additionalelements of information may include one or more categories ofphysiological data as described above. Additional elements ofinformation may include descriptions of particular methods used toobtain biological extractions, such as without limitation physicalextraction of blood samples or the like, capture of data with one ormore sensors, and/or any other information concerning provenance and/orhistory of data acquisition. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various ways in whichdata entries in a biological extraction database 400 may reflectcategories, cohorts, and/or populations of data consistently with thisdisclosure.

Referring now to FIG. 5, one or more database tables in biologicalextraction database 400 may include, as a non-limiting example, a firstprognostic link table 500. First prognostic link table 500 may be atable relating biological extraction data as described above toprognostic labels; for instance, where an expert has entered datarelating a prognostic label to a category of biological extraction dataand/or to an element of biological extraction data via first graphicaluser interface 312 as described above, one or more rows recording suchan entry may be inserted in first prognostic link table 500.Alternatively or additionally, linking of prognostic labels tobiological extraction data may be performed entirely in a prognosticlabel database as described below.

With continued reference to FIG. 5, biological extraction database 400may include tables listing one or more samples and/or biologicalextractions according to sample source. For instance, and withoutlimitation, biological extraction database 400 may include a fluidsample table 504 listing samples acquired from a person by extraction offluids, such as without limitation blood, lymph cerebrospinal fluid, orthe like. As another non-limiting example, biological extractiondatabase 400 may include a sensor data table 508, which may list samplesacquired using one or more sensors, for instance as described in furtherdetail below. As a further non-limiting example, biological extractiondatabase 400 may include a genetic sample table 512, which may listpartial or entire sequences of genetic material. Genetic material may beextracted and amplified, as a non-limiting example, using polymerasechain reactions (PCR) or the like. As a further example, alsonon-limiting, biological extraction database 400 may include a medicalreport table 516, which may list textual descriptions of medical tests,including without limitation radiological tests or tests of strengthand/or dexterity or the like. Data in medical report table may be sortedand/or categorized using a language processing module 416, for instance,translating a textual description into a numerical value and a labelcorresponding to a category of physiological data; this may be performedusing any language processing algorithm or algorithms as referred to inthis disclosure. As another non-limiting example, biological extractiondatabase 400 may include a tissue sample table 520, which may recordbiological extractions obtained using tissue samples. Tables presentedabove are presented for exemplary purposes only; persons skilled in theart will be aware of various ways in which data may be organized inbiological extraction database 400 consistently with this disclosure.

Referring again to FIG. 5, diagnostic engine 108 and/or another devicein diagnostic engine 108 may populate one or more fields in biologicalextraction database 400 using expert information, which may be extractedor retrieved from an expert knowledge database 404. An expert knowledgedatabase 404 may include any data structure and/or data store suitablefor use as a biological extraction database 400 as described above.Expert knowledge database 404 may include data entries reflecting one ormore expert submissions of data such as may have been submittedaccording to any process described above, including without limitationby using first graphical user interface 312 and/or second graphical userinterface 332. Expert knowledge database may include one or more fieldsgenerated by language processing module 416, such as without limitationfields extracted from one or more documents as described above. Forinstance, and without limitation, one or more categories ofphysiological data and/or related prognostic labels and/or categories ofprognostic labels associated with an element of physiological state dataas described above may be stored in generalized from in an expertknowledge database 404 and linked to, entered in, or associated withentries in a biological extraction database 400. Documents may be storedand/or retrieved by diagnostic engine 108 and/or language processingmodule 316 in and/or from a document database 408; document database 408may include any data structure and/or data store suitable for use asbiological extraction database 400 as described above. Documents indocument database 408 may be linked to and/or retrieved using documentidentifiers such as URI and/or URL data, citation data, or the like;persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which documents may beindexed and retrieved according to citation, subject matter, author,date, or the like as consistent with this disclosure.

Referring now to FIG. 6, an exemplary embodiment of an expert knowledgedatabase 404 is illustrated. Expert knowledge database 404 may, as anon-limiting example, organize data stored in the expert knowledgedatabase 404 according to one or more database tables. One or moredatabase tables may be linked to one another by, for instance, commoncolumn values. For instance, a common column between two tables ofexpert knowledge database 404 may include an identifier of an expertsubmission, such as a form entry, textual submission, expert paper, orthe like, for instance as defined below; as a result, a query may beable to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of expert data, including typesof expert data, names and/or identifiers of experts submitting the data,times of submission, or the like; persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which expert data from one or more tables may be linked and/orrelated to expert data in one or more other tables.

Still referring to FIG. 6, one or more database tables in expertknowledge database 404 may include, as a non-limiting example, an expertprognostic table 600. Expert prognostic table 600 may be a tablerelating biological extraction data as described above to prognosticlabels; for instance, where an expert has entered data relating aprognostic label to a category of biological extraction data and/or toan element of biological extraction data via first graphical userinterface 312 as described above, one or more rows recording such anentry may be inserted in expert prognostic table 600. In an embodiment,a forms processing module 604 may sort data entered in a submission viafirst graphical user interface 312 by, for instance, sorting data fromentries in the first graphical user interface 312 to related categoriesof data; for instance, data entered in an entry relating in the firstgraphical user interface 312 to a prognostic label may be sorted intovariables and/or data structures for storage of prognostic labels, whiledata entered in an entry relating to a category of physiological dataand/or an element thereof may be sorted into variables and/or datastructures for the storage of, respectively, categories of physiologicaldata or elements of physiological data. Where data is chosen by anexpert from pre-selected entries such as drop-down lists, data may bestored directly; where data is entered in textual form, languageprocessing module 316 may be used to map data to an appropriate existinglabel, for instance using a vector similarity test or othersynonym-sensitive language processing test to map physiological data toan existing label. Alternatively or additionally, when a languageprocessing algorithm, such as vector similarity comparison, indicatesthat an entry is not a synonym of an existing label, language processingmodule may indicate that entry should be treated as relating to a newlabel; this may be determined by, e.g., comparison to a threshold numberof cosine similarity and/or other geometric measures of vectorsimilarity of the entered text to a nearest existent label, anddetermination that a degree of similarity falls below the thresholdnumber and/or a degree of dissimilarity falls above the thresholdnumber. Data from expert textual submissions 608, such as accomplishedby filling out a paper or PDF form and/or submitting narrativeinformation, may likewise be processed using language processing module416. Data may be extracted from expert papers 612, which may includewithout limitation publications in medical and/or scientific journals,by language processing module 316 via any suitable process as describedherein. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various additional methods whereby novelterms may be separated from already-classified terms and/or synonymstherefore, as consistent with this disclosure. Expert prognostic table600 may include a single table and/or a plurality of tables; pluralityof tables may include tables for particular categories of prognosticlabels such as a current diagnosis table, a future prognosis table, agenetic tendency table, a metabolic tendency table, and/or an endocrinaltendency table (not shown), to name a few non-limiting examplespresented for illustrative purposes only.

With continued reference to FIG. 6, one or more database tables inexpert knowledge database 404 may include, as a further non-limitingexample tables listing one or more alimentary instruction set processlabels; expert data populating such tables may be provided, withoutlimitation, using any process described above, including entry of datafrom second graphical user interface 332 via forms processing module 604and/or language processing module 416, processing of textual submissions608, or processing of expert papers 612. For instance, and withoutlimitation, an ameliorative alimentary table 616 may list one or morealimentary instruction set processes based on alimentary instructions,and/or links of such one or more alimentary instruction set processes toprognostic labels, as provided by experts according to any method ofprocessing and/or entering expert data as described above. As a furtherexample an ameliorative action table 620 may list one or more alimentaryinstruction set processes based on instructions for actions a usershould take, including without limitation exercise, meditation, and/orcessation of harmful eating, substance abuse, or other habits, and/orlinks of such one or more alimentary instruction set processes toprognostic labels, as provided by experts according to any method ofprocessing and/or entering expert data as described above. As anadditional example, an alimentary supplement table 628 may list one ormore alimentary instruction set processes based on alimentarysupplements, such as vitamin pills or the like, and/or links of such oneor more alimentary instruction set processes to prognostic labels, asprovided by experts according to any method of processing and/orentering expert data as described above. As a further non-limitingexample, an ameliorative supplement table 628 may list one or morealimentary instruction set processes based on medications, includingwithout limitation over-the-counter and prescription pharmaceuticaldrugs, and/or links of such one or more alimentary instruction setprocesses to prognostic labels, as provided by experts according to anymethod of processing and/or entering expert data as described above. Asan additional example, a counterindication table 632 may list one ormore counter-indications for one or more alimentary instruction setprocesses; counterindications may include, without limitation allergiesto one or more foods, medications, and/or supplements, side-effects ofone or more medications and/or supplements, interactions betweenmedications, foods, and/or supplements, exercises that should not beused given one or more medical conditions, injuries, disabilities,and/or demographic categories, or the like. Tables presented above arepresented for exemplary purposes only; persons skilled in the art willbe aware of various ways in which data may be organized in expertknowledge database 404 consistently with this disclosure.

Referring again to FIG. 4, a prognostic label database 412, which may beimplemented in any manner suitable for implementation of biologicalextraction database 400, may be used to store prognostic labels used indiagnostic engine 108, including any prognostic labels correlated withelements of physiological data in first training set 300 as describedabove; prognostic labels may be linked to or refer to entries inbiological extraction database 400 to which prognostic labelscorrespond. Linking may be performed by reference to historical dataconcerning biological extractions, such as diagnoses, prognoses, and/orother medical conclusions derived from biological extractions in thepast; alternatively or additionally, a relationship between a prognosticlabel and a data entry in biological extraction database 400 may bedetermined by reference to a record in an expert knowledge database 404linking a given prognostic label to a given category of biologicalextraction as described above. Entries in prognostic label database 412may be associated with one or more categories of prognostic labels asdescribed above, for instance using data stored in and/or extracted froman expert knowledge database 404.

Referring now to FIG. 7, an exemplary embodiment of a prognostic labeldatabase 412 is illustrated. Prognostic label database 412 may, as anon-limiting example, organize data stored in the prognostic labeldatabase 412 according to one or more database tables. One or moredatabase tables may be linked to one another by, for instance, commoncolumn values. For instance, a common column between two tables ofprognostic label database 412 may include an identifier of an expertsubmission, such as a form entry, textual submission, expert paper, orthe like, for instance as defined below; as a result, a query may beable to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of expert data, including typesof expert data, names and/or identifiers of experts submitting the data,times of submission, or the like; persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which expert data from one or more tables may be linked and/orrelated to expert data in one or more other tables.

Still referring to FIG. 7, one or more database tables in prognosticlabel database 412 may include, as a non-limiting example, an extractiondata table 700. Extraction data table 700 may be a table listing sampledata, along with, for instance, one or more linking columns to link suchdata to other information stored in prognostic label database 412. In anembodiment, extraction data 704 may be acquired, for instance frombiological extraction database 400, in a raw or unsorted form, and maybe translated into standard forms, such as standard units ofmeasurement, labels associated with particular physiological datavalues, or the like; this may be accomplished using a datastandardization module 708, which may perform unit conversions. Datastandardization module 708 may alternatively or additionally map textualinformation, such as labels describing values tested for or the like,using language processing module 316 or equivalent components and/oralgorithms thereto.

Continuing to refer to FIG. 7, prognostic label database 412 may includean extraction label table 712; extraction label table 712 may listprognostic labels received with and/or extracted from biologicalextractions, for instance as received in the form of extraction text716. A language processing module 316 may compare textual information soreceived to prognostic labels and/or form new prognostic labelsaccording to any suitable process as described above. Extractionprognostic link table 720 may combine extractions with prognosticlabels, as acquired from extraction label table and/or expert knowledgedatabase 404; combination may be performed by listing together in rowsor by relating indices or common columns of two or more tables to eachother. Tables presented above are presented for exemplary purposes only;persons skilled in the art will be aware of various ways in which datamay be organized in expert knowledge database 404 consistently with thisdisclosure.

Referring again to FIG. 4, first training set 300 may be populated byretrieval of one or more records from biological extraction database 400and/or prognostic label database 412; in an embodiment, entriesretrieved from biological extraction database 400 and/or prognosticlabel database 412 may be filtered and or select via query to match oneor more additional elements of information as described above, so as toretrieve a first training set 300 including data belonging to a givencohort, demographic population, or other set, so as to generate outputsas described below that are tailored to a person or persons with regardto whom diagnostic engine 108 classifies biological extractions toprognostic labels as set forth in further detail below. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various ways in which records may be retrieved from biologicalextraction database 400 and/or prognostic label database to generate afirst training set to reflect individualized group data pertaining to aperson of interest in operation of system and/or method, includingwithout limitation a person with regard to whom at least a biologicalextraction is being evaluated as described in further detail below.Diagnostic engine 108 may alternatively or additionally receive a firsttraining set 300 and store one or more entries in biological extractiondatabase 400 and/or prognostic label database 412 as extracted fromelements of first training set 300.

Still referring to FIG. 4, diagnostic engine 108 may include orcommunicate with an ameliorative process label database 416; anameliorative process label database 416 may include any data structureand/or datastore suitable for use as a biological extraction database400 as described above. An ameliorative process label database 416 mayinclude one or more entries listing labels associated with one or morealimentary instruction set processes as described above, including anyalimentary instruction set labels correlated with prognostic labels insecond training set 320 as described above; alimentary instruction setprocess labels may be linked to or refer to entries in prognostic labeldatabase 412 to which alimentary instruction set process labelscorrespond. Linking may be performed by reference to historical dataconcerning prognostic labels, such as therapies, food coaching/nutritioncounseling, treatments, and/or lifestyle or dietary choices chosen toalleviate conditions associated with prognostic labels in the past;alternatively or additionally, a relationship between an alimentaryinstruction set process label and a data entry in prognostic labeldatabase 412 may be determined by reference to a record in an expertknowledge database 404 linking a given alimentary instruction setprocess label to a given category of prognostic label as describedabove. Entries in prognostic label database 412 may be associated withone or more categories of prognostic labels as described above, forinstance using data stored in and/or extracted from an expert knowledgedatabase 304.

Referring now to FIG. 8, an exemplary embodiment of an ameliorativeprocess label database 416 is illustrated. Ameliorative process labeldatabase 416 may, as a non-limiting example, organize data stored in theameliorative process label database 416 according to one or moredatabase tables. One or more database tables may be linked to oneanother by, for instance, common column values. For instance, a commoncolumn between two tables of ameliorative process label database 416 mayinclude an identifier of an expert submission, such as a form entry,textual submission, expert paper, or the like, for instance as definedbelow; as a result, a query may be able to retrieve all rows from anytable pertaining to a given submission or set thereof. Other columns mayinclude any other category usable for organization or subdivision ofexpert data, including types of expert data, names and/or identifiers ofexperts submitting the data, times of submission, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which expert data from one or more tablesmay be linked and/or related to expert data in one or more other tables.

Still referring to FIG. 8, ameliorative process label database 416 mayinclude a second prognostic link table 800; second prognostic link table800 may link ameliorative process data to prognostic label data, usingany suitable method for linking data in two or more tables as describedabove. Ameliorative process label database 416 may include anameliorative alimentary table 804, which may list one or more alimentaryinstruction set processes based on alimentary instructions, and/or linksof such one or more alimentary instruction set processes to prognosticlabels, for instance as provided by experts according to any method ofprocessing and/or entering expert data as described above, and/or usingone or more machine-learning processes as set forth in further detailbelow. As a further example an ameliorative action table 808 may listone or more alimentary instruction set processes based on instructionsfor actions a user should take, including without limitation cessationof harmful eating, substance abuse, or other habits, and/or links ofsuch one or more alimentary instruction set processes to prognosticlabels, as provided by experts according to any method of processingand/or entering expert data as described above and/or using one or moremachine-learning processes as set forth in further detail below. As anadditional example, an ameliorative supplement table 812 may list one ormore alimentary instruction set processes based on alimentarysupplements, such as vitamin pills or the like, and/or links of such oneor more alimentary instruction set processes to prognostic labels, asprovided by experts according to any method of processing and/orentering expert data as described above and/or using one or moremachine-learning processes as set forth in further detail below. As afurther non-limiting example, an ameliorative medication table 816 maylist one or more alimentary instruction set processes based onmedications, including without limitation over-the-counter andprescription pharmaceutical drugs, and/or links of such one or morealimentary instruction set processes to prognostic labels, as providedby experts according to any method of processing and/or entering expertdata as described above and/or using one or more machine-learningprocesses as set forth in further detail below. As an additionalexample, a counterindication table 820 may list one or morecounter-indications for one or more alimentary instruction setprocesses; counterindications may include, without limitation allergiesto one or more foods, medications, and/or supplements, side-effects ofone or more medications and/or supplements, interactions betweenmedications, foods, and/or supplements, or the like; this may beacquired using expert submission as described above and/or using one ormore machine-learning processes as set forth in further detail below.Tables presented above are presented for exemplary purposes only;persons skilled in the art will be aware of various ways in which datamay be organized in databases of system 100 consistently with thisdisclosure.

Referring again to FIG. 4, second training set 320 may be populated byretrieval of one or more records from prognostic label database 412and/or ameliorative process label database 416; in an embodiment,entries retrieved from prognostic label database 412 and/or ameliorativeprocess label database 416 may be filtered and or select via query tomatch one or more additional elements of information as described above,so as to retrieve a second training set 320 including data belonging toa given cohort, demographic population, or other set, so as to generateoutputs as described below that are tailored to a person or persons withregard to whom diagnostic engine 108 classifies prognostic labels toalimentary instruction set process labels as set forth in further detailbelow. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which records may beretrieved from prognostic label database 412 and/or ameliorative processlabel database 416 to generate a second training set 320 to reflectindividualized group data pertaining to a person of interest inoperation of system and/or method, including without limitation a personwith regard to whom at least a biological extraction is being evaluatedas described in further detail below. Diagnostic engine 108 mayalternatively or additionally receive a second training set 320 andstore one or more entries in prognostic label database 412 and/orameliorative process label database 416 as extracted from elements ofsecond training set 320.

In an embodiment, and still referring to FIG. 4, diagnostic engine 108may receive an update to one or more elements of data represented infirst training set 300 and/or second training set 320, and may performone or more modifications to first training set 300 and/or secondtraining set 320, or to biological extraction database 400, expertknowledge database 404, prognostic label database 412, and/orameliorative process label database 416 as a result. For instance abiological extraction may turn out to have been erroneously recorded;diagnostic engine 108 may remove it from first training set 300, secondtraining set 320, biological extraction database 400, expert knowledgedatabase 404, prognostic label database 412, and/or ameliorative processlabel database 416 as a result. As a further example, a medical and/oracademic paper, or a study on which it was based, may be revoked;diagnostic engine 108 may remove it from first training set 300, secondtraining set 320, biological extraction database 400, expert knowledgedatabase 404, prognostic label database 412, and/or ameliorative processlabel database 416 as a result. Information provided by an expert maylikewise be removed if the expert loses credentials or is revealed tohave acted fraudulently.

Continuing to refer to FIG. 4, elements of data first training set 300,second training set 320, biological extraction database 400, expertknowledge database 404, prognostic label database 412, and/orameliorative process label database 416 may have temporal attributes,such as timestamps; diagnostic engine 108 may order such elementsaccording to recency, select only elements more recently entered forfirst training set 300 and/or second training set 320, or otherwise biastraining sets, database entries, and/or machine-learning models asdescribed in further detail below toward more recent or less recententries. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which temporal attributesof data entries may be used to affect results of methods and/or systemsas described herein.

Referring again to FIG. 3, diagnostic engine 108 may be configured torecord at least a biological extraction. At least a biologicalextraction may include a physically extracted sample, which as usedherein includes a sample obtained by removing and analyzing tissueand/or fluid. Physically extracted sample may include without limitationa blood sample, a tissue sample, a buccal swab, a mucous sample, a stoolsample, a hair sample, a fingernail sample, or the like. Physicallyextracted sample may include, as a non-limiting example, at least ablood sample. As a further non-limiting example, at least a biologicalextraction may include at least a genetic sample. At least a geneticsample may include a complete genome of a person or any portion thereof.At least a genetic sample may include a DNA sample and/or an RNA sample.At least a biological extraction may include an epigenetic sample, aproteomic sample, a tissue sample, a biopsy, and/or any other physicallyextracted sample. At least a biological extraction may include anendocrinal sample. As a further non-limiting example, at least abiological extraction may include a signal from at least a sensorconfigured to detect physiological data of a user and recording the atleast a biological extraction as a function of the signal. At least asensor may include any medical sensor and/or medical device configuredto capture sensor data concerning a patient, including any scanning,radiological and/or imaging device such as without limitation x-rayequipment, computer assisted tomography (CAT) scan equipment, positronemission tomography (PET) scan equipment, any form of magnetic resonanceimagery (MRI) equipment, ultrasound equipment, optical scanningequipment such as photo-plethysmographic equipment, or the like. Atleast a sensor may include any electromagnetic sensor, including withoutlimitation electroencephalographic sensors, magnetoencephalographicsensors, electrocardiographic sensors, electromyographic sensors, or thelike. At least a sensor may include a temperature sensor. At least asensor may include any sensor that may be included in a mobile deviceand/or wearable device, including without limitation a motion sensorsuch as an inertial measurement unit (IMU), one or more accelerometers,one or more gyroscopes, one or more magnetometers, or the like. At leasta wearable and/or mobile device sensor may capture step, gait, and/orother mobility data, as well as data describing activity levels and/orphysical fitness. At least a wearable and/or mobile device sensor maydetect heart rate or the like. At least a sensor may detect anyhematological parameter including blood oxygen level, pulse rate, heartrate, pulse rhythm, and/or blood pressure. At least a sensor may be apart of diagnostic engine 108 or may be a separate device incommunication with diagnostic engine 108.

Still referring to FIG. 3, at least a biological extraction may includeany data suitable for use as physiological state data as describedabove, including without limitation any result of any medical test,physiological assessment, cognitive assessment, psychologicalassessment, or the like. System 100 may receive at least a biologicalextraction from one or more other devices after performance; system 100may alternatively or additionally perform one or more assessments and/ortests to obtain at least a biological extraction, and/or one or moreportions thereof, on system 100. For instance, at least biologicalextraction may include or more entries by a user in a form or similargraphical user interface object; one or more entries may include,without limitation, user responses to questions on a psychological,behavioral, personality, or cognitive test. For instance, at least aserver 104 may present to user a set of assessment questions designed orintended to evaluate a current state of mind of the user, a currentpsychological state of the user, a personality trait of the user, or thelike; at least a server 104 may provide user-entered responses to suchquestions directly as at least a biological extraction and/or mayperform one or more calculations or other algorithms to derive a scoreor other result of an assessment as specified by one or more testingprotocols, such as automated calculation of a Stanford-Binet and/orWechsler scale for IQ testing, a personality test scoring such as aMyers-Briggs test protocol, or other assessments that may occur topersons skilled in the art upon reviewing the entirety of thisdisclosure.

Alternatively or additionally, and with continued reference to FIG. 3,at least a biological extraction may include assessment and/orself-assessment data, and/or automated or other assessment results,obtained from a third-party device; third-party device may include,without limitation, a server or other device (not shown) that performsautomated cognitive, psychological, behavioral, personality, or otherassessments. Third-party device may include a device operated by aninformed advisor.

Referring to FIG. 4, at least a biological extraction may include datadescribing one or more test results, including results of mobilitytests, stress tests, dexterity tests, endocrinal tests, genetic tests,and/or electromyographic tests, biopsies, radiological tests, genetictests, and/or sensory tests. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalexamples of at least a biological extraction consistent with thisdisclosure. At least a biological extraction may be added to biologicalextraction database 400.

With continued reference to FIG. 4, diagnostic engine 108 may include aprognostic label learner 336 operating on the diagnostic engine 108, theprognostic label learner 336 designed and configured to generate the atleast a prognostic output as a function of the first training set 300and the at least a biological extraction. Prognostic label learner 336may include any hardware and/or software module. Prognostic labellearner 336 is designed and configured to generate outputs using machinelearning processes. A machine learning process is a process thatautomatedly uses a body of data known as “training data” and/or a“training set” to generate an algorithm that will be performed by acomputing device/module to produce outputs given data provided asinputs; this is in contrast to a non-machine learning software programwhere the commands to be executed are determined in advance by a userand written in a programming language.

Referring now to FIG. 9, prognostic label learner 336 may be designedand configured to generate at least a prognostic output by creating atleast a first machine-learning model 340 relating physiological statedata 304 to prognostic labels using the first training set 300 andgenerating the at least a prognostic output using the firstmachine-learning model 340; at least a first machine-learning model 340may include one or more models that determine a mathematicalrelationship between physiological state data 304 and prognostic labels.Such models may include without limitation model developed using linearregression models. Linear regression models may include ordinary leastsquares regression, which aims to minimize the square of the differencebetween predicted outcomes and actual outcomes according to anappropriate norm for measuring such a difference (e.g. a vector-spacedistance norm); coefficients of the resulting linear equation may bemodified to improve minimization. Linear regression models may includeridge regression methods, where the function to be minimized includesthe least-squares function plus term multiplying the square of eachcoefficient by a scalar amount to penalize large coefficients. Linearregression models may include least absolute shrinkage and selectionoperator (LASSO) models, in which ridge regression is combined withmultiplying the least-squares term by a factor of 1 divided by doublethe number of samples. Linear regression models may include a multi-tasklasso model wherein the norm applied in the least-squares term of thelasso model is the Frobenius norm amounting to the square root of thesum of squares of all terms. Linear regression models may include theelastic net model, a multi-task elastic net model, a least angleregression model, a LARS lasso model, an orthogonal matching pursuitmodel, a Bayesian regression model, a logistic regression model, astochastic gradient descent model, a perceptron model, a passiveaggressive algorithm, a robustness regression model, a Huber regressionmodel, or any other suitable model that may occur to persons skilled inthe art upon reviewing the entirety of this disclosure. Linearregression models may be generalized in an embodiment to polynomialregression models, whereby a polynomial equation (e.g. a quadratic,cubic or higher-order equation) providing a best predicted output/actualoutput fit is sought; similar methods to those described above may beapplied to minimize error functions, as will be apparent to personsskilled in the art upon reviewing the entirety of this disclosure.

Continuing to refer to FIG. 9, machine-learning algorithm used togenerate first machine-learning model 340 may include, withoutlimitation, linear discriminant analysis. Machine-learning algorithm mayinclude quadratic discriminate analysis. Machine-learning algorithms mayinclude kernel ridge regression. Machine-learning algorithms may includesupport vector machines, including without limitation support vectorclassification-based regression processes. Machine-learning algorithmsmay include stochastic gradient descent algorithms, includingclassification and regression algorithms based on stochastic gradientdescent. Machine-learning algorithms may include nearest neighborsalgorithms. Machine-learning algorithms may include Gaussian processessuch as Gaussian Process Regression. Machine-learning algorithms mayinclude cross-decomposition algorithms, including partial least squaresand/or canonical correlation analysis. Machine-learning algorithms mayinclude naïve Bayes methods. Machine-learning algorithms may includealgorithms based on decision trees, such as decision tree classificationor regression algorithms. Machine-learning algorithms may includeensemble methods such as bagging meta-estimator, forest of randomizedtress, AdaBoost, gradient tree boosting, and/or voting classifiermethods. Machine-learning algorithms may include neural net algorithms,including convolutional neural net processes.

Still referring to FIG. 9, prognostic label learner 336 may generateprognostic output using alternatively or additional artificialintelligence methods, including without limitation by creating anartificial neural network, such as a convolutional neural networkcomprising an input layer of nodes, one or more intermediate layers, andan output layer of nodes. Connections between nodes may be created viathe process of “training” the network, in which elements from a trainingdataset are applied to the input nodes, a suitable training algorithm(such as Levenberg-Marquardt, conjugate gradient, simulated annealing,or other algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning. This network may be trained using first trainingset 300; the trained network may then be used to apply detectedrelationships between elements of physiological state data 304 andprognostic labels.

Still referring to FIG. 9, machine-learning algorithms used byprognostic label learner 336 may include supervised machine-learningalgorithms, which may, as a non-limiting example be executed using asupervised learning module 900 executing on diagnostic engine 108 and/oron another computing device in communication with diagnostic engine 108,which may include any hardware or software module. Supervised machinelearning algorithms, as defined herein, include algorithms that receivea training set relating a number of inputs to a number of outputs, andseek to find one or more mathematical relations relating inputs tooutputs, where each of the one or more mathematical relations is optimalaccording to some criterion specified to the algorithm using somescoring function. For instance, a supervised learning algorithm may useelements of physiological data as inputs, prognostic labels as outputs,and a scoring function representing a desired form of relationship to bedetected between elements of physiological data and prognostic labels;scoring function may, for instance, seek to maximize the probabilitythat a given element of physiological state data 304 and/or combinationof elements of physiological data is associated with a given prognosticlabel and/or combination of prognostic labels to minimize theprobability that a given element of physiological state data 304 and/orcombination of elements of physiological state data 304 is notassociated with a given prognostic label and/or combination ofprognostic labels. Scoring function may be expressed as a risk functionrepresenting an “expected loss” of an algorithm relating inputs tooutputs, where loss is computed as an error function representing adegree to which a prediction generated by the relation is incorrect whencompared to a given input-output pair provided in first training set300. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various possible variations of supervisedmachine learning algorithms that may be used to determine relationbetween elements of physiological data and prognostic labels. In anembodiment, one or more supervised machine-learning algorithms may berestricted to a particular domain for instance, a supervisedmachine-learning process may be performed with respect to a given set ofparameters and/or categories of parameters that have been suspected tobe related to a given set of prognostic labels, and/or are specified aslinked to a medical specialty and/or field of medicine covering aparticular set of prognostic labels. As a non-limiting example, aparticular set of blood test biomarkers and/or sensor data may betypically used by cardiologists to diagnose or predict variouscardiovascular conditions, and a supervised machine-learning process maybe performed to relate those blood test biomarkers and/or sensor data tothe various cardiovascular conditions; in an embodiment, domainrestrictions of supervised machine-learning procedures may improveaccuracy of resulting models by ignoring artifacts in training data.Domain restrictions may be suggested by experts and/or deduced fromknown purposes for particular evaluations and/or known tests used toevaluate prognostic labels. Additional supervised learning processes maybe performed without domain restrictions to detect, for instance,previously unknown and/or unsuspected relationships betweenphysiological data and prognostic labels.

Still referring to FIG. 9, machine-learning algorithms may includeunsupervised processes; unsupervised processes may, as a non-limitingexample, be executed by an unsupervised learning module 904 executing ondiagnostic engine 108 and/or on another computing device incommunication with diagnostic engine 108, which may include any hardwareor software module. An unsupervised machine-learning process, as usedherein, is a process that derives inferences in datasets without regardto labels; as a result, an unsupervised machine-learning process may befree to discover any structure, relationship, and/or correlationprovided in the data. For instance, and without limitation, prognosticlabel learner 336 and/or diagnostic engine 108 may perform anunsupervised machine learning process on first training set 300, whichmay cluster data of first training set 300 according to detectedrelationships between elements of the first training set 300, includingwithout limitation correlations of elements of physiological state data304 to each other and correlations of prognostic labels to each other;such relations may then be combined with supervised machine learningresults to add new criteria for prognostic label learner 336 to apply inrelating physiological state data 304 to prognostic labels. As anon-limiting, illustrative example, an unsupervised process maydetermine that a first element of physiological data acquired in a bloodtest correlates closely with a second element of physiological data,where the first element has been linked via supervised learningprocesses to a given prognostic label, but the second has not; forinstance, the second element may not have been defined as an input forthe supervised learning process, or may pertain to a domain outside of adomain limitation for the supervised learning process. Continuing theexample a close correlation between first element of physiological statedata 304 and second element of physiological state data 304 may indicatethat the second element is also a good predictor for the prognosticlabel; second element may be included in a new supervised process toderive a relationship or may be used as a synonym or proxy for the firstphysiological element by prognostic label learner 436.

Still referring to FIG. 9, diagnostic engine 108 and/or prognostic labellearner 336 may detect further significant categories of physiologicaldata, relationships of such categories to prognostic labels, and/orcategories of prognostic labels using machine-learning processes,including without limitation unsupervised machine-learning processes asdescribed above; such newly identified categories, as well as categoriesentered by experts in free-form fields as described above, may be addedto pre-populated lists of categories, lists used to identify languageelements for language learning module, and/or lists used to identifyand/or score categories detected in documents, as described above. In anembodiment, as additional data is added to diagnostic engine 108,prognostic label learner 336 and/or diagnostic engine 108 maycontinuously or iteratively perform unsupervised machine-learningprocesses to detect relationships between different elements of theadded and/or overall data; in an embodiment, this may enable diagnosticengine 108 to use detected relationships to discover new correlationsbetween known biomarkers, prognostic labels, and/or alimentaryinstruction set labels and one or more elements of data in large bodiesof data, such as genomic, proteomic, and/or microbiome-related data,enabling future supervised learning and/or lazy learning processes asdescribed in further detail below to identify relationships between,e.g., particular clusters of genetic alleles and particular prognosticlabels and/or suitable alimentary instruction set labels. Use ofunsupervised learning may greatly enhance the accuracy and detail withwhich system may detect prognostic labels and/or alimentary instructionset labels.

With continued reference to FIG. 9, unsupervised processes may besubjected to domain limitations. For instance, and without limitation,an unsupervised process may be performed regarding a comprehensive setof data regarding one person, such as a comprehensive medical history,set of test results, and/or physiological data such as genomic,proteomic, and/or other data concerning that persons. As anothernon-limiting example, an unsupervised process may be performed on dataconcerning a particular cohort of persons; cohort may include, withoutlimitation, a demographic group such as a group of people having ashared age range, ethnic background, nationality, sex, and/or gender.Cohort may include, without limitation, a group of people having ashared value for an element and/or category of physiological data, agroup of people having a shared value for an element and/or category ofprognostic label, and/or a group of people having a shared value and/orcategory of alimentary instruction set label; as illustrative examples,cohort could include all people having a certain level or range oflevels of blood triglycerides, all people diagnosed with type IIdiabetes, all people who regularly run between 10 and 15 miles per week,or the like. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of a multiplicity of ways in whichcohorts and/or other sets of data may be defined and/or limited for aparticular unsupervised learning process.

Still referring to FIG. 9, prognostic label learner 336 mayalternatively or additionally be designed and configured to generate atleast a prognostic output by executing a lazy learning process as afunction of the first training set 300 and the at least a biologicalextraction; lazy learning processes may be performed by a lazy learningmodule 908 executing on diagnostic engine 108 and/or on anothercomputing device in communication with diagnostic engine 108, which mayinclude any hardware or software module. A lazy-learning process and/orprotocol, which may alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover a “first guess” ata prognostic label associated with biological extraction, using firsttraining set 300. As a non-limiting example, an initial heuristic mayinclude a ranking of prognostic labels according to relation to a testtype of at least a biological extraction, one or more categories ofphysiological data identified in test type of at least a biologicalextraction, and/or one or more values detected in at least a biologicalextraction; ranking may include, without limitation, ranking accordingto significance scores of associations between elements of physiologicaldata and prognostic labels, for instance as calculated as describedabove. Heuristic may include selecting some number of highest-rankingassociations and/or prognostic labels. Prognostic label learner 336 mayalternatively or additionally implement any suitable “lazy learning”algorithm, including without limitation a K-nearest neighbors algorithm,a lazy naïve Bayes algorithm, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variouslazy-learning algorithms that may be applied to generate prognosticoutputs as described in this disclosure, including without limitationlazy learning applications of machine-learning algorithms as describedin further detail below.

In an embodiment, and continuing to refer to FIG. 10, prognostic labellearner 336 may generate a plurality of prognostic labels havingdifferent implications for a particular person. For instance, where theat least a biological extraction includes a result of a dexterity test,a low score may be consistent with amyotrophic lateral sclerosis,Parkinson's disease, multiple sclerosis, and/or any number of less severdisorders or tendencies associated with lower levels of dexterity. Insuch a situation, prognostic label learner 336 and/or diagnostic engine108 may perform additional processes to resolve ambiguity. Processes mayinclude presenting multiple possible results to a medical practitioner,informing the medical practitioner that one or more follow-up testsand/or biological extractions are needed to further determine a moredefinite prognostic label. Alternatively or additionally, processes mayinclude additional machine learning steps; for instance, where referenceto a model generated using supervised learning on a limited domain hasproduced multiple mutually exclusive results and/or multiple resultsthat are unlikely all to be correct, or multiple different supervisedmachine learning models in different domains may have identifiedmutually exclusive results and/or multiple results that are unlikely allto be correct. In such a situation, prognostic label learner 336 and/ordiagnostic engine 108 may operate a further algorithm to determine whichof the multiple outputs is most likely to be correct; algorithm mayinclude use of an additional supervised and/or unsupervised model.Alternatively or additionally, prognostic label learner 336 may performone or more lazy learning processes using a more comprehensive set ofuser data to identify a more probably correct result of the multipleresults. Results may be presented and/or retained with rankings, forinstance to advise a medical professional of the relative probabilitiesof various prognostic labels being correct; alternatively oradditionally, prognostic labels associated with a probability ofcorrectness below a given threshold and/or prognostic labelscontradicting results of the additional process, may be eliminated. As anon-limiting example, an endocrinal test may determine that a givenperson has high levels of dopamine, indicating that a poor pegboardperformance is almost certainly not being caused by Parkinson's disease,which may lead to Parkinson's being eliminated from a list of prognosticlabels associated with poor pegboard performance, for that person.Similarly, a genetic test may eliminate Huntington's disease, or anotherdisease definitively linked to a given genetic profile, as a cause.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which additional processingmay be used to determine relative likelihoods of prognostic labels on alist of multiple prognostic labels, and/or to eliminate some labels fromsuch a list. Prognostic output 812 may be provided to user output deviceas described in further detail below.

Referring now to FIG. 10, diagnostic engine 108 includes an ameliorativeprocess label learner 344 operating on the diagnostic engine 108, theameliorative process label learner 344 designed and Referring again toFIG. 4, diagnostic engine 108 may include an ameliorative process labellearner 344 operating on the diagnostic engine 108, the ameliorativeprocess label learner 344 designed and configured to generate the atleast an ameliorative output as a function of the second training set320 and the at least a prognostic output. Ameliorative process labellearner 344 may include any hardware or software module suitable for useas a prognostic label learner 336 as described above. Ameliorativeprocess label learner 344 is a machine-learning module as describedabove; ameliorative process label learner 344 may perform anymachine-learning process or combination of processes suitable for use bya prognostic label learner 336 as described above. For instance, andwithout limitation, and ameliorative process label learner 344 may beconfigured to create a second machine-learning model 348 relatingprognostic labels to ameliorative labels using the second training set320 and generate the at least an ameliorative output using the secondmachine-learning model 348; second machine-learning model 348 may begenerated according to any process, process steps, or combination ofprocesses and/or process steps suitable for creation of first machinelearning model. In an embodiment, ameliorative process label learner 344may use data from first training set 300 as well as data from secondtraining set 320; for instance, ameliorative process label learner 344may use lazy learning and/or model generation to determine relationshipsbetween elements of physiological data, in combination with or insteadof prognostic labels, and ameliorative labels. Where ameliorativeprocess label learner 344 determines relationships between elements ofphysiological data and ameliorative labels directly, this may determinerelationships between prognostic labels and ameliorative labels as wellowing to the existence of relationships determined by prognostic labellearner 336.

Referring to FIG. 10, ameliorative process label learner 344 may beconfigured to perform one or more supervised learning processes, asdescribed above; supervised learning processes may be performed by asupervised learning module 1000 executing on diagnostic engine 108and/or on another computing device in communication with diagnosticengine 108, which may include any hardware or software module. Forinstance, a supervised learning algorithm may use prognostic labels asinputs, ameliorative labels as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweenprognostic labels and ameliorative labels; scoring function may, forinstance, seek to maximize the probability that a given prognostic labeland/or combination of prognostic labels is associated with a givenameliorative label and/or combination of ameliorative labels to minimizethe probability that a given prognostic label and/or combination ofprognostic labels is not associated with a given ameliorative labeland/or combination of ameliorative labels. In an embodiment, one or moresupervised machine-learning algorithms may be restricted to a particulardomain; for instance, a supervised machine-learning process may beperformed with respect to a given set of parameters and/or categories ofprognostic labels that have been suspected to be related to a given setof ameliorative labels, for instance because the ameliorative processescorresponding to the set of ameliorative labels are hypothesized orsuspected to have an ameliorative effect on conditions represented bythe prognostic labels, and/or are specified as linked to a medicalspecialty and/or field of medicine covering a particular set ofprognostic labels and/or ameliorative labels. As a non-limiting example,a particular set prognostic labels corresponding to a set ofcardiovascular conditions may be typically treated by cardiologists, anda supervised machine-learning process may be performed to relate thoseprognostic labels to ameliorative labels associated with varioustreatment options, medications, and/or lifestyle changes.

With continued reference to FIG. 10, ameliorative process label learner344 may perform one or more unsupervised machine-learning processes asdescribed above; unsupervised processes may be performed by anunsupervised learning module 1004 executing on diagnostic engine 108and/or on another computing device in communication with diagnosticengine 108, which may include any hardware or software module. Forinstance, and without limitation, ameliorative process label learner 344and/or diagnostic engine 108 may perform an unsupervised machinelearning process on second training set 320, which may cluster data ofsecond training set 320 according to detected relationships betweenelements of the second training set 320, including without limitationcorrelations of prognostic labels to each other and correlations ofameliorative labels to each other; such relations may then be combinedwith supervised machine learning results to add new criteria forameliorative process label learner 344 to apply in relating prognosticlabels to ameliorative labels. As a non-limiting, illustrative example,an unsupervised process may determine that a first prognostic label 308correlates closely with a second prognostic label 324, where the firstprognostic label 308 has been linked via supervised learning processesto a given ameliorative label, but the second has not; for instance, thesecond prognostic label 324 may not have been defined as an input forthe supervised learning process, or may pertain to a domain outside of adomain limitation for the supervised learning process. Continuing theexample, a close correlation between first prognostic label 308 andsecond prognostic label 324 may indicate that the second prognosticlabel 324 is also a good match for the ameliorative label; secondprognostic label 324 may be included in a new supervised process toderive a relationship or may be used as a synonym or proxy for the firstprognostic label 308 by ameliorative process label learner 444.Unsupervised processes performed by ameliorative process label learner344 may be subjected to any domain limitations suitable for unsupervisedprocesses performed by prognostic label learner 336 as described above.

Still referring to FIG. 10, diagnostic engine 108 and/or ameliorativeprocess label learner 344 may detect further significant categories ofprognostic labels, relationships of such categories to ameliorativelabels, and/or categories of ameliorative labels using machine-learningprocesses, including without limitation unsupervised machine-learningprocesses as described above; such newly identified categories, as wellas categories entered by experts in free-form fields as described above,may be added to pre-populated lists of categories, lists used toidentify language elements for language learning module, and/or listsused to identify and/or score categories detected in documents, asdescribed above. In an embodiment, as additional data is added todiagnostic engine 108, ameliorative process label learner 344 and/ordiagnostic engine 108 may continuously or iteratively performunsupervised machine-learning processes to detect relationships betweendifferent elements of the added and/or overall data; in an embodiment,this may enable diagnostic engine 108 to use detected relationships todiscover new correlations between known biomarkers, prognostic labels,and/or ameliorative labels and one or more elements of data in largebodies of data, such as genomic, proteomic, and/or microbiome-relateddata, enabling future supervised learning and/or lazy learning processesto identify relationships between, e.g., particular clusters of geneticalleles and particular prognostic labels and/or suitable ameliorativelabels. Use of unsupervised learning may greatly enhance the accuracyand detail with which system may detect prognostic labels and/orameliorative labels.

Continuing to view FIG. 10, ameliorative process label learner 344 maybe configured to perform a lazy learning process as a function of thesecond training set 320 and the at least a prognostic output to producethe at least an ameliorative output; a lazy learning process may includeany lazy learning process as described above regarding prognostic labellearner 336. Lazy learning processes may be performed by a lazy learningmodule 1108 executing on diagnostic engine 108 and/or on anothercomputing device in communication with diagnostic engine 108, which mayinclude any hardware or software module. Ameliorative output 1012 may beprovided to a user output device as described in further detail below.

In an embodiment, and still referring to FIG. 10, ameliorative processlabel learner 344 may generate a plurality of ameliorative labels havingdifferent implications for a particular person. For instance, where aprognostic label indicates that a person has a magnesium deficiency,various dietary choices may be generated as ameliorative labelsassociated with correcting the deficiency, such as ameliorative labelsassociated with consumption of almonds, spinach, and/or dark chocolate,as well as ameliorative labels associated with consumption of magnesiumsupplements. In such a situation, ameliorative process label learner 344and/or diagnostic engine 108 may perform additional processes to resolveambiguity. Processes may include presenting multiple possible results toa medical practitioner, informing the medical practitioner of variousoptions that may be available, and/or that follow-up tests, procedures,or counseling may be required to select an appropriate choice.Alternatively or additionally, processes may include additional machinelearning steps. For instance, ameliorative process label learner 344 mayperform one or more lazy learning processes using a more comprehensiveset of user data to identify a more probably correct result of themultiple results. Results may be presented and/or retained withrankings, for instance to advise a medical professional of the relativeprobabilities of various ameliorative labels being correct or idealchoices for a given person; alternatively or additionally, ameliorativelabels associated with a probability of success or suitability below agiven threshold and/or ameliorative labels contradicting results of theadditional process, may be eliminated. As a non-limiting example, anadditional process may reveal that a person is allergic to tree nuts,and consumption of almonds may be eliminated as an ameliorative label tobe presented.

Continuing to refer to FIG. 10, ameliorative process label learner 344may be designed and configured to generate further training data and/orto generate outputs using longitudinal data 1116. As used herein,longitudinal data 1016 may include a temporally ordered series of dataconcerning the same person, or the same cohort of persons; for instance,longitudinal data 1016 may describe a series of blood samples taken oneday or one month apart over the course of a year. Longitudinal data 1016may related to a series of samples tracking response of one or moreelements of physiological data recorded regarding a person undergoingone or more ameliorative processes linked to one or more ameliorativeprocess labels. Ameliorative process label learner 344 may track one ormore elements of physiological data and fit, for instance, a linear,polynomial, and/or splined function to data points; linear, polynomial,or other regression across larger sets of longitudinal data, using, forinstance, any regression process as described above, may be used todetermine a best-fit graph or function for the effect of a givenameliorative process over time on a physiological parameter. Functionsmay be compared to each other to rank ameliorative processes; forinstance, an ameliorative process associated with a steeper slope incurve representing improvement in a physiological data element, and/or ashallower slope in a curve representing a slower decline, may be rankedhigher than an ameliorative process associated with a less steep slopefor an improvement curve or a steeper slope for a curve marking adecline. Ameliorative processes associated with a curve and/or terminaldata point representing a value that does not associate with apreviously detected prognostic label may be ranked higher than one thatis not so associated. Information obtained by analysis of longitudinaldata 1016 may be added to ameliorative process database and/or secondtraining set.

Referring again to FIG. 3, diagnostic engine 108 may include analimentary instruction label learner 352 operating on the diagnosticengine 108, the alimentary instruction label learner 352 designed andconfigured to generate at least an alimentary data output as a functionof the second training set 320 and the at least a prognostic output.Alimentary instruction label learner 352 may include any hardware orsoftware module suitable for use as a prognostic label learner 336 asdescribed above. Alimentary instruction label learner 352 may include amachine-learning module as described above; alimentary instruction labellearner 352 may perform any machine-learning process or combination ofprocesses suitable for use by a prognostic label learner 336 asdescribed above. For instance, and without limitation, and alimentaryinstruction label learner 352 may be configured to create a thirdmachine-learning model 356 relating prognostic labels to alimentarylabels using the second training set 320 and generate the at least analimentary data output using the third machine-learning model 356; thirdmachine-learning model 356 may be generated according to any process,process steps, or combination of processes and/or process steps suitablefor creation of first machine learning model. In an embodiment,alimentary instruction label learner 352 may use data from firsttraining set 300 as well as data from second training set 320; forinstance, alimentary instruction label learner 352 may use lazy learningand/or model generation to determine relationships between elements ofphysiological data, in combination with or instead of prognostic labels,and alimentary labels, which may include, without limitation, a subsetof ameliorative labels corresponding to alimentary processes. Wherealimentary instruction label learner 352 determines relationshipsbetween elements of physiological data and alimentary labels directly,this may determine relationships between prognostic labels andalimentary labels as well owing to the existence of relationshipsdetermined by prognostic label learner 336.

Referring now to FIG. 11, alimentary instruction label learner 352 maybe configured to perform one or more supervised learning processes, asdescribed above; supervised learning processes may be performed by asupervised learning module 1100 executing on diagnostic engine 108and/or on another computing device in communication with diagnosticengine 108, which may include any hardware or software module. Forinstance, a supervised learning algorithm may use prognostic labels asinputs, alimentary labels as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweenprognostic labels and alimentary labels; scoring function may, forinstance, seek to maximize the probability that a given prognostic labeland/or combination of prognostic labels is associated with a givenalimentary label and/or combination of alimentary labels to minimize theprobability that a given prognostic label and/or combination ofprognostic labels is not associated with a given alimentary label and/orcombination of alimentary labels. In an embodiment, one or moresupervised machine-learning algorithms may be restricted to a particulardomain; for instance, a supervised machine-learning process may beperformed with respect to a given set of parameters and/or categories ofprognostic labels that have been suspected to be related to a given setof alimentary labels, for instance because the alimentary processescorresponding to the set of alimentary labels are hypothesized orsuspected to have an ameliorative effect on conditions represented bythe prognostic labels, and/or are specified as linked to a medicalspecialty and/or field of medicine covering a particular set ofprognostic labels and/or alimentary labels. As a non-limiting example, aparticular set prognostic labels corresponding to a set ofcardiovascular conditions may be typically treated by cardiologists, anda supervised machine-learning process may be performed to relate thoseprognostic labels to alimentary labels associated with variousalimentary options.

With continued reference to FIG. 11, alimentary instruction labellearner 352 may perform one or more unsupervised machine-learningprocesses as described above; unsupervised processes may be performed byan unsupervised learning module 1104 executing on diagnostic engine 108and/or on another computing device in communication with diagnosticengine 108, which may include any hardware or software module. Forinstance, and without limitation, alimentary instruction label learner352 and/or diagnostic engine 108 may perform an unsupervised machinelearning process on second training set 320, which may cluster data ofsecond training set 320 according to detected relationships betweenelements of the second training set 320, including without limitationcorrelations of prognostic labels to each other and correlations ofalimentary labels to each other; such relations may then be combinedwith supervised machine learning results to add new criteria foralimentary instruction label learner 352 to apply in relating prognosticlabels to alimentary labels. As a non-limiting, illustrative example, anunsupervised process may determine that a first prognostic label 308correlates closely with a second prognostic label 324, where the firstprognostic label 308 has been linked via supervised learning processesto a given alimentary label, but the second has not; for instance, thesecond prognostic label 324 may not have been defined as an input forthe supervised learning process, or may pertain to a domain outside of adomain limitation for the supervised learning process. Continuing theexample, a close correlation between first prognostic label 308 andsecond prognostic label 324 may indicate that the second prognosticlabel 324 is also a good match for the alimentary label; secondprognostic label 324 may be included in a new supervised process toderive a relationship or may be used as a synonym or proxy for the firstprognostic label 308 by alimentary instruction label learner 452.Unsupervised processes performed by alimentary instruction label learner352 may be subjected to any domain limitations suitable for unsupervisedprocesses performed by prognostic label learner 336 as described above.

Still referring to FIG. 11, diagnostic engine 108 and/or alimentaryinstruction label learner 352 may detect further significant categoriesof prognostic labels, relationships of such categories to alimentarylabels, and/or categories of alimentary labels using machine-learningprocesses, including without limitation unsupervised machine-learningprocesses as described above; such newly identified categories, as wellas categories entered by experts in free-form fields as described above,may be added to pre-populated lists of categories, lists used toidentify language elements for language learning module, and/or listsused to identify and/or score categories detected in documents, asdescribed above. In an embodiment, as additional data is added todiagnostic engine 108, alimentary instruction label learner 352 and/ordiagnostic engine 108 may continuously or iteratively performunsupervised machine-learning processes to detect relationships betweendifferent elements of the added and/or overall data; in an embodiment,this may enable diagnostic engine 108 to use detected relationships todiscover new correlations between known biomarkers, prognostic labels,and/or alimentary labels and one or more elements of data in largebodies of data, such as genomic, proteomic, and/or microbiome-relateddata, enabling future supervised learning and/or lazy learning processesto identify relationships between, e.g., particular clusters of geneticalleles and particular prognostic labels and/or suitable alimentarylabels. Use of unsupervised learning may greatly enhance the accuracyand detail with which system may detect prognostic labels and/oralimentary labels.

Continuing to view FIG. 11, alimentary instruction label learner 352 maybe configured to perform a lazy learning process as a function of thesecond training set 320 and the at least a prognostic output to producethe at least an alimentary output; a lazy learning process may includeany lazy learning process as described above regarding prognostic labellearner 436. Lazy learning processes may be performed by a lazy learningmodule 1108 executing on diagnostic engine 108 and/or on anothercomputing device in communication with diagnostic engine 108, which mayinclude any hardware or software module. Alimentary output 1112 may beprovided to a user output device as described in further detail below.

In an embodiment, and still referring to FIG. 12, alimentary instructionlabel learner 352 may generate a plurality of alimentary labels havingdifferent implications for a particular person. For instance, where aprognostic label indicates that a person has a magnesium deficiency,various dietary choices may be generated as alimentary labels associatedwith correcting the deficiency, such as alimentary labels associatedwith consumption of almonds, spinach, and/or dark chocolate, as well asalimentary labels associated with consumption of magnesium supplements.In such a situation, alimentary instruction label learner 352 and/ordiagnostic engine 108 may perform additional processes to resolveambiguity. Processes may include presenting multiple possible results toa medical practitioner, informing the medical practitioner of variousoptions that may be available, and/or that follow-up tests, procedures,or counseling may be required to select an appropriate choice.Alternatively or additionally, processes may include additional machinelearning steps. For instance, alimentary instruction label learner 352may perform one or more lazy learning processes using a morecomprehensive set of user data to identify a more probably correctresult of the multiple results. Results may be presented and/or retainedwith rankings, for instance to advise a medical professional of therelative probabilities of various alimentary labels being correct orideal choices for a given person; alternatively or additionally,alimentary labels associated with a probability of success orsuitability below a given threshold and/or alimentary labelscontradicting results of the additional process, may be eliminated. As anon-limiting example, an additional process may reveal that a person isallergic to tree nuts, and consumption of almonds may be eliminated asan alimentary label to be presented.

Continuing to refer to FIG. 11, alimentary instruction label learner 352may be designed and configured to generate further training data and/orto generate outputs using longitudinal data 1116. As used herein,Longitudinal data 1116 may include a temporally ordered series of dataconcerning the same person, or the same cohort of persons; for instance,Longitudinal data 1116 may describe a series of blood samples taken oneday or one month apart over the course of a year. Longitudinal data 1116may relate to a series of samples tracking response of one or moreelements of physiological data recorded regarding a person undergoingone or more alimentary processes linked to one or more alimentaryprocess labels. Alimentary instruction label learner 352 may track oneor more elements of physiological data and fit, for instance, a linear,polynomial, and/or splined function to data points; linear, polynomial,or other regression across larger sets of longitudinal data, using, forinstance, any regression process as described above, may be used todetermine a best-fit graph or function for the effect of a givenalimentary process over time on a physiological parameter. Functions maybe compared to each other to rank alimentary processes; for instance, analimentary process associated with a steeper slope in curve representingimprovement in a physiological data element, and/or a shallower slope ina curve representing a slower decline, may be ranked higher than analimentary process associated with a less steep slope for an improvementcurve or a steeper slope for a curve marking a decline. Alimentaryprocesses associated with a curve and/or terminal data pointrepresenting a value that does not associate with a previously detectedprognostic label may be ranked higher than one that is not soassociated. Information obtained by analysis of Longitudinal data 1116may be added to alimentary process database and/or second training set.

Embodiments of diagnostic engine 108 may furnish augmented intelligencesystems that facilitate diagnostic, prognostic, curative, and/ortherapeutic decisions by nutrition, diet, and wellness professionalssuch as nutritionists, dieticians, or applicabletrainers/coaches/mentors. Diagnostic engine 108 may provide fullyautomated tools and resources for each applicable professional tohandle, process, diagnosis, develop alimentary, diet, or wellness plans,facilitate and monitor all patient implementation, and record eachpatient status. Provision of expert system elements via expert inputsand document-driven language analysis may ensure that recommendationsgenerated by diagnostic engine 108 are backed by the very best medicaland alimentary knowledge and practices in the world. Models and/orlearners with access to data in depth may enable generation ofrecommendations that are directly personalized for each patient,providing complete confidence, mitigated risk, and completetransparency. Access to well-organized and personalized knowledge indepth may greatly enhance efficiency of nutrition consultations; inembodiments, a comprehensive session may be completed in as little as 10minutes. Recommendations may further suggest follow up testing, therapy,and/or delivery of substances, ensuring an effective ongoing treatmentand prognostic plan.

Referring again to FIG. 1, vibrant constitutional network system 100includes a plan generation module 112 operating on the at least a server104. Plan generation module 112 may include any suitable hardware orhardware module. In an embodiment, plan generation module 112 isdesigned and configured to generate a Comprehensive instruction set 206associated with the user based on the diagnostic output. In anembodiment, Comprehensive instruction set 206 is a data structurecontaining instructions to be provided to the user to explain the user'scurrent prognostic status, as reflected by one or more prognosticoutputs and provide the user with a plan based on the at least analimentary instruction set output, to achieve that. Comprehensiveinstruction set 206 may include but is not limited to a program,strategy, summary, recommendation, or any other type of interactiveplatform that may be configured to comprise information associated withthe user, an applicable verified external source, and one or moreoutputs derived from the analyses performed on the extraction from theuser. Comprehensive instruction set 206 may describe to a user a futureprognostic status to aspire to.

Referring now to FIG. 12, an exemplary embodiment of a plan generationmodule 112 is illustrated. Comprehensive instruction set 206 includes atleast a current prognostic descriptor 1200 which as used in thisdisclosure is an element of data describing a current prognostic statusbased on at least one prognostic output. Plan generation module 112 mayproduce at least a current prognostic descriptor 1200 using at least aprognostic output. In an embodiment, plan generation module 112 mayinclude a label synthesizer 1304. Label synthesizer 1204 may include anysuitable software or hardware module. In an embodiment, labelsynthesizer 1204 may be designed and configured to combine a pluralityof labels in at least a prognostic output together to provide maximallyefficient data presentation. Combination of labels together may includeelimination of duplicate information. For instance, label synthesizer1204 and/or at least a server 104 may be designed and configure todetermine a first prognostic label of the at least a prognostic label isa duplicate of a second prognostic label of the at least a prognosticlabel and eliminate the first prognostic label. Determination that afirst prognostic label is a duplicate of a second prognostic label mayinclude determining that the first prognostic label is identical to thesecond prognostic label; for instance, a prognostic label generated fromtest data presented in one biological extraction of at least abiological extraction may be the same as a prognostic label generatedfrom test data presented in a second biological extraction of at least abiological extraction. As a further non-limiting example, a firstprognostic label may be synonymous with a second prognostic label, wheredetection of synonymous labels may be performed, without limitation, bya language processing module 316 as described above.

Continuing to refer to FIG. 12, label synthesizer 1204 may groupprognostic labels according to one or more classification systemsrelating the prognostic labels to each other. For instance, plangeneration module 112 and/or label synthesizer 1204 may be configured todetermine that a first prognostic label of the at least a prognosticlabel and a second prognostic label of the at least a prognostic labelbelong to a shared category. A shared category may be a category ofconditions or tendencies toward a future condition to which each offirst prognostic label and second prognostic label belongs; as anexample, lactose intolerance and gluten sensitivity may each be examplesof digestive sensitivity, for instance, which may in turn share acategory with food sensitivities, food allergies, digestive disorderssuch as celiac disease and diverticulitis, or the like. Shared categoryand/or categories may be associated with prognostic labels as well. Agiven prognostic label may belong to a plurality of overlappingcategories. Plan generation module 112 may be configured to add acategory label associated with a shared category to Comprehensiveinstruction set 206, where addition of the label may include addition ofthe label and/or a datum linked to the label, such as a textual ornarrative description. In an embodiment, relationships betweenprognostic labels and categories may be retrieved from a prognosticlabel classification database 1208, for instance by generating a queryusing one or more prognostic labels of at least a prognostic output,entering the query, and receiving one or more categories matching thequery from the prognostic label classification database 1208.

Referring now to FIG. 13, an exemplary embodiment of a prognostic labelclassification database 1208 is illustrated. Prognostic labelclassification database 1208 may be implemented as any database and/ordatastore suitable for use as biological extraction database 400 asdescribed above. One or more database tables in prognostic labelclassification database 1208 may include, without limitation, asymptomatic classification table 1300; symptomatic classification table1300 may relate each prognostic label to one or more categories ofsymptoms associated with that prognostic label. As a non-limitingexample, symptomatic classification table 1300 may include recordsindicating that each of lactose intolerance and gluten sensitivityresults in symptoms including gas buildup, bloating, and abdominal pain.One or more database tables in prognostic label classification database1208 may include, without limitation, a systemic classification table1204; systemic classification table 1304 may relate each prognosticlabel to one or more systems associated with that prognostic label. As anon-limiting example, systemic classification table 1304 may includerecords indicating each of lactose intolerance and gluten sensitivityaffects the digestive system; two digestive sensitivities linked toallergic or other immune responses may additionally be linked insystemic classification table 1304 to the immune system. One or moredatabase tables in prognostic label classification database 1208 mayinclude, without limitation, a body part classification table 1208; bodypart classification table 1308 may relate each prognostic label to oneor more body parts associated with that prognostic label. As anon-limiting example, body part classification table 1308 may includerecords indicating each of psoriasis and rosacea affects the skin of aperson. One or more database tables in prognostic label classificationdatabase 1208 may include, without limitation, a causal classificationtable 1312; causal classification table 1312 may relate each prognosticlabel to one or more causes associated with that prognostic label. As anon-limiting example, causal classification table 1312 may includerecords indicating each of type 2 diabetes and hypertension may haveobesity as a cause. The above-described tables, and entries therein, areprovided solely for exemplary purposes. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousadditional examples for tables and/or relationships that may be includedor recorded in prognostic classification table consistently with thisdisclosure.

Referring again to FIG. 12, plan generation module 112 may be configuredto generate current prognostic descriptor 1200 by converting one or moreprognostic labels into narrative language. As a non-limiting example,plan generation module 112 may include a narrative language unit 1212,which may be configured to determine an element of narrative languageassociated with at least a prognostic label and include the element ofnarrative language in current prognostic label descriptor. Narrativelanguage unit 1212 may implement this, without limitation, by using alanguage processing module 316 to detect one or more associationsbetween prognostic labels, or lists of prognostic labels, and phrasesand/or statements of narrative language. Alternatively or additionally,Narrative language unit 1212 may retrieve one or more elements ofnarrative language from a narrative language database 1316, which maycontain one or more tables associating prognostic labels and/or groupsof prognostic labels with words, sentences, and/or phrases of narrativelanguage. One or more elements of narrative language may be included inComprehensive instruction set 206, for instance for display to a user astext describing a current prognostic status of the user. Currentprognostic descriptor 1200 may further include one or more images; oneor more images may be retrieved by plan generation module 112 from animage database 1120, which may contain one or more tables associatingprognostic labels, groups of prognostic labels, current prognosticdescriptors 1200, or the like with one or more images.

With continued reference to FIG. 12, Comprehensive instruction set 206may include one or more follow-up suggestions, which may include,without limitation, suggestions for acquisition of an additionalbiological extraction; in an embodiment, additional biologicalextraction may be provided to diagnostic engine 108, which may triggerrepetition of one or more processes as described above, includingwithout limitation generation of prognostic output, refinement orelimination of ambiguous prognostic labels of prognostic output,generation of alimentary instruction set output, and/or refinement orelimination of ambiguous alimentary instruction set labels of alimentaryinstruction set output. For instance, where a pegboard test resultsuggests possible diagnoses of Parkinson's disease, Huntington'sdisease, ALS, and MS as described above, follow-up suggestions mayinclude suggestions to perform endocrinal tests, genetic tests, and/orelectromyographic tests; results of such tests may eliminate one or moreof the possible diagnoses, such that a subsequently displayed outputonly lists conditions that have not been eliminated by the follow-uptest. Follow-up tests may include any receipt of any biologicalextraction as described above.

With continued reference to FIG. 12, comprehensive instruction set mayinclude one or more elements of contextual information, includingwithout limitation any patient medical history such as current labresults, a current reason for visiting a medical professional, currentstatus of one or more currently implemented treatment plans,biographical information concerning the patient, and the like. One ormore elements of contextual information may include goals a patientwishes to achieve with a medical visit or session, and/or as result ofinteraction with diagnostic engine 108. Contextual information mayinclude one or more questions a patient wishes to have answered in amedical visit and/or session, and/or as a result of interaction withdiagnostic engine 108. Contextual information may include one or morequestions to ask a patient. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various forms ofcontextual information that may be included, consistently with thisdisclosure.

With continued reference to FIG. 12, Comprehensive instruction set 206may include at least a future prognostic descriptor 1224. As usedherein, a future prognostic descriptor 1224 is an element of datadescribing a future prognostic status based on at least one prognosticoutput, which may include without limitation a desired furtherprognostic status. In an embodiment, future prognostic descriptor 1224may include any element suitable for inclusion in current prognosticdescriptor 1200. Future prognostic descriptor 1224 may be generatedusing any processes, modules, and/or components suitable for generationof current prognostic descriptor 1200 as described above.

Still referring to FIG. 12, comprehensive instruction set 206 includesat least an alimentary instruction set descriptor 1228, which as definedin this disclosure an element of data describing one or more alimentaryinstruction set processes to be followed based on at least onealimentary instruction set output; at least an alimentary instructionset process descriptor 1328 may include descriptors for alimentaryinstruction set processes usable to achieve future prognostic descriptor1224. Plan generation module 112 may receive at least an alimentaryinstruction set descriptor 1228 from alimentary instruction setgenerator module 120.

Continuing to refer to FIG. 12, plan generation module 112 may beconfigured to receive at least an element of user data and filterdiagnostic output using the at least an element of user data. At leastan element of user data, as used herein, is any element of datadescribing the user, user needs, and/or user preferences. At least anelement of user data may include a constitutional restriction. At leasta constitutional restriction may include any health-based reason that auser may be unable to engage in a given alimentary instruction setprocess; at least a constitutional restriction may include anycounter-indication as described above, including an injury, a diagnosisof something preventing use of one or more alimentary instruction setprocesses, an allergy or food-sensitivity issue, a medication that iscounter-indicated, or the like. At least an element of user data mayinclude at least a user preference. At least a user preference mayinclude, without limitation, any preference to engage in or eschew anyalimentary instruction set process and/or other potential elements of acomprehensive instruction set 206, including religious preferences suchas forbidden foods, medical interventions, exercise routines, or thelike.

Still referring to FIG. 12, comprehensive instruction set 206 includesat least an ameliorative process descriptor 1232, which as defined inthis disclosure an element of data describing one or more ameliorativeprocesses to be followed based on at least one ameliorative output; atleast an ameliorative process descriptor 1232 may include descriptorsfor ameliorative processes usable to achieve future prognosticdescriptor 1024. Plan generation module 112 may produce at least anameliorative process descriptor 1232 using at least a prognostic output.In an embodiment, label synthesizer 1204 may be designed and configuredto combine a plurality of labels in at least an ameliorative outputtogether to provide maximally efficient data presentation. Combinationof labels together may include elimination of duplicate information. Forinstance, label synthesizer 1204 and/or at least a server 104 may bedesigned and configure to determine a first ameliorative label of the atleast an ameliorative label is a duplicate of a second ameliorativelabel of the at least an ameliorative label and eliminate the firstameliorative label. Determination that a first ameliorative label is aduplicate of a second ameliorative label may include determining thatthe first ameliorative label is identical to the second ameliorativelabel; for instance, a ameliorative label generated from test datapresented in one biological extraction of at least a biologicalextraction may be the same as a ameliorative label generated from testdata presented in a second biological extraction of at least abiological extraction. As a further non-limiting example, a firstameliorative label may be synonymous with a second ameliorative label,where detection of synonymous labels may be performed, withoutlimitation, by a language processing module 216 as described above.

Continuing to refer to FIG. 12, label synthesizer 1204 may groupameliorative labels according to one or more classification systemsrelating the ameliorative labels to each other. For instance, plangeneration module 112 and/or label synthesizer 1204 may be configured todetermine that a first ameliorative label of the at least anameliorative label and a second ameliorative label of the at least anameliorative label belong to a shared category. A shared category may bea category of conditions or tendencies toward a future condition towhich each of first ameliorative label and second ameliorative labelbelongs; as an example, lactose intolerance and gluten sensitivity mayeach be examples of digestive sensitivity, for instance, which may inturn share a category with food sensitivities, food allergies, digestivedisorders such as celiac disease and diverticulitis, or the like. Sharedcategory and/or categories may be associated with ameliorative labels aswell. A given ameliorative label may belong to a plurality ofoverlapping categories. Plan generation module 112 may be configured toadd a category label associated with a shared category to comprehensiveinstruction set 206, where addition of the label may include addition ofthe label and/or a datum linked to the label, such as a textual ornarrative description. In an embodiment, relationships betweenameliorative labels and categories may be retrieved from an ameliorativelabel classification database 1236, for instance by generating a queryusing one or more ameliorative labels of at least an ameliorativeoutput, entering the query, and receiving one or more categoriesmatching the query from the ameliorative label classification database1236.

Referring now to FIG. 14, an exemplary embodiment of an ameliorativelabel classification database 1236 is illustrated. Ameliorative labelclassification database 1236 may be implemented as any database and/ordatastore suitable for use as biological extraction database 400 asdescribed above. One or more database tables in ameliorative labelclassification database 1236 may include, without limitation, anintervention category table 1400; an intervention may relate eachameliorative label to one or more categories associated with thatameliorative label. As a non-limiting example, intervention categorytable 1400 may include records indicating that each of a plan to consumea given quantity of almonds and a plan to consume less meat maps to acategory of nutritional instruction, while a plan to jog for 30 minutesper day maps to a category of activity. One or more database tables inameliorative label classification database 1236 may include, withoutlimitation, a alimentary category table 1404; alimentary category table1404 may relate each ameliorative label pertaining to nutrition to oneor more categories associated with that ameliorative label. As anon-limiting example, alimentary category table 1404 may include recordsindicating that each of a plan to consume more almonds and a plan toconsume more walnuts qualifies as a plan to consume more nuts, as wellas a plan to consume more protein. One or more database tables inameliorative label classification database 1236 may include, withoutlimitation, an action category table 1208; action category table 1408may relate each ameliorative label pertaining to an action to one ormore categories associated with that ameliorative label. As anon-limiting example, action category table 1408 may include recordsindicating that each of a plan jog for 30 minutes a day and a plan toperform a certain number of sit-ups per day qualifies as an exerciseplan. One or more database tables in ameliorative label classificationdatabase 1236 may include, without limitation, a medication categorytable 1412; medication category table 1412 may relate each ameliorativelabel associated with a medication to one or more categories associatedwith that ameliorative label. As a non-limiting example, medicationcategory table 1412 may include records indicating that each of a planto take an antihistamine and a plan to take an anti-inflammatory steroidbelongs to a category of allergy medications. One or more databasetables in ameliorative label classification database 1236 may include,without limitation, a supplement category table 1416; supplementcategory table 1416 may relate each ameliorative label pertaining to asupplement to one or more categories associated with that ameliorativelabel. As a non-limiting example, supplement category table 1416 mayinclude records indicating that each of a plan to consume a calciumsupplement and a plan to consume a vitamin D supplement corresponds to acategory of supplements to aid in bone density. Ameliorative labels maybe mapped to each of alimentary category table 1404, action categorytable 1408, supplement category table 1416, and medication categorytable 1412 using intervention category table 1400. The above-describedtables, and entries therein, are provided solely for exemplary purposes.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various additional examples for tablesand/or relationships that may be included or recorded in ameliorativeclassification table consistently with this disclosure.

Referring again to FIG. 1, system 100 includes an alimentary instructionset generator module 120 operating on at least a server 104. Alimentaryinstruction set generator module 120 may include any hardware orsoftware module suitable for use as a plan generator module 112.Alimentary instruction set generator module may interact with plangenerator module 112. For instance, and without limitation, alimentaryinstruction set generator module 120 may be configured to generate,based on Comprehensive instruction set 206, an alimentary instructionset 124 associated with the user.

In one embodiment, Comprehensive instruction set 206 includes at leastone or more elements of contextual information, including withoutlimitation any patient medical history such as current lab results, acurrent reason for visiting a medical professional, current status ofone or more currently implemented treatment plans, biographicalinformation concerning the patient, and the like. One or more elementsof contextual information may include goals a patient wishes to achievewith a medical visit or session, and/or as result of interaction withdiagnostic engine 108. Contextual information may include one or morequestions a patient wishes to have answered in a medical visit and/orsession, and/or as a result of interaction with diagnostic engine 108.Contextual information may include one or more questions to ask apatient. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various forms of contextual informationthat may be included, consistently with this disclosure.

Referring now to FIG. 15, an exemplary embodiment of a narrativelanguage database 1216 is illustrated. Narrative language database 1216may be implemented as any database and/or datastore suitable for use asbiological extraction database 400 as described above. One or moredatabase tables in narrative language database 1216 may include, withoutlimitation, a prognostic description table 1300, which may linkprognostic labels to narrative descriptions associated with prognosticlabels. One or more database tables in narrative language database 1216may include, without limitation, an alimentary instruction setdescription table 1508, which may link alimentary instruction setprocess labels to narrative descriptions associated with alimentaryinstruction set process labels. One or more database tables in narrativelanguage database 1216 may include, without limitation, a combineddescription table 1308, which may link combinations of prognostic labelsand alimentary instruction set labels to narrative descriptionsassociated with the combinations. One or more database tables innarrative language database 1216 may include, without limitation, aparagraph template table 1516, which may contain one or more templatesof paragraphs, pages, reports, or the like into which images and text,such as images obtained from Image database 1220 and text obtained fromprognostic description table 1300, alimentary instruction setdescription table 1508, and combined description table 1308 may beinserted. Tables in narrative description database 1116 may bepopulated, as a non-limiting example, using submissions from experts,which may be collected according to any processes described above.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various way sin which entries in narrativedescription database 1116 may be categorized and/or organized.

Referring now to FIG. 16, an exemplary embodiment of an image database1220 is illustrated. Image database 1220 may be implemented as anydatabase and/or datastore suitable for use as biological extractiondatabase 400 as described above. One or more database tables in imagedatabase 1220 may include, without limitation, a prognostic image table1600, which may link prognostic labels to images associated withprognostic labels. One or more database tables in image database 1220may include, without limitation, an alimentary image table 1604, whichmay link alimentary instruction set process labels to images associatedwith alimentary instruction set process labels. One or more databasetables in Image database 1220 may include, without limitation, acombined image table 1608, which may link combinations of prognosticlabels and alimentary instruction set labels to images associated withthe combinations. One or more database tables in image database 102 mayinclude, without limitation, a prognostic video table 1612, which maylink prognostic labels to videos associated with prognostic labels. Oneor more database tables in Image database 1220 may include, withoutlimitation, an alimentary instruction set video table 1416, which maylink alimentary instruction set process labels to videos associated withalimentary instruction set process labels. One or more database tablesin Image database 1220 may include, without limitation, a combined videotable 1620, which may link combinations of prognostic labels andalimentary instruction set labels to videos associated with thecombinations. Tables in Image database 1220 may be populated, withoutlimitation, by submissions by experts, which may be provided accordingto any process or process steps described in this disclosure forcollection of expert submissions.

Referring to FIG. 17, an exemplary embodiment of a user database 1236 isillustrated. User database 1236 may be implemented as any databaseand/or datastore suitable for use as biological extraction database 400as described above. One or more database tables in user database 1236may include, without limitation, a constitution restriction table 1700;at least a constitutional restriction may be linked to a given userand/or user identifier in a constitutional restriction table 1700. Oneor more database tables in user database 1236 may include, withoutlimitation, a user preference table 1704; at least a user preference maybe linked to a given user and/or user identifier in a user preferencetable 1704.

Referring again to FIG. 1, system 100 includes an alimentary instructionset generator module 120 operating on at least a server 104. Alimentaryinstruction set generator module 120 may include any hardware orsoftware module suitable for use as a plan generator module 112.Alimentary instruction set generator module may interact with plangenerator module 112. For instance, and without limitation, alimentaryinstruction set generator module 120 may be configured to generate,based on comprehensive instruction set 206, an alimentary instructionset 124 associated with the user.

Referring now to FIG. 18, an exemplary embodiment of an alimentaryinstruction set generator module 120 is illustrated. In one embodiment,the alimentary instruction set generator module 120 may be configured togenerate an alimentary instruction set comprising a plurality ofinformation reflecting a comprehensive list of meals, supplements, andprocesses aimed towards resolving any identified issues, suggestions, ordeficiencies discoverable via the Comprehensive instruction set 206.Alimentary instruction set generator module 120 may be configured togenerate an alimentary instruction set associated with user 202 thatautomatically interacts with a plurality of performances and processes(as illustrated in FIG. 2 via Performances 210-216) and generates a setof orders or instructions configured to be processed by Performances210-216. Alimentary instruction set generator module 120 may produce atleast an alimentary instruction set process descriptor 1028 using atleast an alimentary instruction set output. In an embodiment, Alimentaryinstruction set generator module may include a label synthesizer 1204 asdescribed above.

In one embodiment, and still referring to FIG. 18, the alimentaryinstruction set 208 may be presented to user 202 via a graphical userinterface coupled to user client device 132 associated with user 202operating in or subscribing to network 200. Alimentary instruction setgenerator module 120 is further configured to export data to externaldestinations based on the categorization of components of alimentaryinstruction set 124 within performances 210-216. Alimentary instructionset 124 is configured to interact with one or more servers associatedwith performances 210-216 by vibrant constitutional network 200establishing a communicatively coupling relationship between thealimentary instruction set and respective physical performance entityservers 306 configured to execute a physical performance instruction setassociated with respective performances 210-216. Performances 210-216may be but are not limited to any food preparation performances, fooddelivery performances, vitamin/supplement coaching service, healthsupplement delivery service, grocery delivery service, or any otherapplicable platform configured for preparation and delivery of itemsrelating to food/nutrition, health, and wellness.

Continuing to refer to FIG. 18, alimentary instruction set generatormodule 120 is designed and configured to an alimentary instruction set124 based on comprehensive instruction set 206. In an embodiment,alimentary instruction set generator module 120 may generate alimentaryinstruction set 124 based on the integration of data associated withcomprehensive instruction set 206, any applicable external sources, andany applicable database within system 100 or physical performance entitynetwork 302. Generation of alimentary instruction set 124 may includeidentification of one or more alimentary instructions in comprehensiveinstruction set, and insertion of the one or more alimentaryinstructions in the alimentary instruction set 124; for instance,alimentary instruction set 124 may be formed, wholly or partially, byaggregating alimentary instructions from Comprehensive instruction set206 and combining the aggregated alimentary instructions using narrativelanguage module, narrative language database, image database, or thelike, according to any process suitable for generation of comprehensiveinstruction set as described above.

In one embodiment, and with continued reference to FIG. 18, alimentaryinstruction set generator module 120 may generate alimentary instructionset 124 based on alimentary data and non-alimentary data in order tofacilitate both medicinal and holistic components in alimentaryinstruction set 124 specific to user 202. In one embodiment, alimentarydata may be identified and aggregated into a subset of applicablealimentary data based on at least a biological extraction 204 andcomprehensive instruction set 206. In application, alimentaryinstruction set 124 may comprise a plurality of alimentary data specificto user 202 that is able to be used by machine learning and artificialintelligence systems in order to continuously update or modify trainingsets, and ultimately Comprehensive instruction set 206 and alimentaryinstruction set 124 based on updated or progressions associated withimplementation of alimentary instruction set 124 by user 202. Alimentarydata and non-alimentary data may include compilations of instructionsets received over a period of time, the compilations may account forimprovements or modifications associated with user 202. Alimentaryinstruction set 124 may further include instructions over time, in whichthe alimentary instructions may change in response to changes in auser's data and/or prognosis. Alternatively or additionally, system 100may periodically iterate through one or more processes as described inthis disclosure, such that repeated reevaluations may modify alimentaryinstruction set 124 as information concerning user and/or biologicalextractions obtained from the user change over time.

In one embodiment, and still referring to FIG. 18, alimentaryinstruction set generator module 120 may identify a non-alimentaryinstruction within comprehensive instruction set 206, determine analimentary analog to the non-alimentary instruction and introduce thealimentary analog into the alimentary instruction set and/or use thealimentary analog to update the physical performance instruction set. Analimentary analog, as used herein, is an alimentary process orinstruction that achieves a similar purpose to a non-alimentary processand/or instruction. As a non-limiting example, certain foods such asgrapefruit may act to lower blood sugar; where the impact of consuming aparticular quantity of such foods is similar to or the same as an impactof consuming a blood sugar medication, the former may be an alimentaryanalog of the latter. In one embodiment, non-alimentary data withinComprehensive instruction set 206 may be subsequently substituted inalimentary instruction set 124 with alimentary data configured toprovide user 202 with holistic solutions to issues that were initiallytreated with non-holistic approaches. For example, if initiallydiagnostic output indicates that the blood sugar of user 202 isabnormally high then Comprehensive instruction set 206 may recommendthat user 202 take applicable medications classified as non-alimentaryin order to lower the blood sugar immediately. The process of user 202receiving the applicable medications may be based on execution of one ofperformances 210-216 by physical performance entity 304. However,alimentary instruction set 124 may subsequently or concurrently provideone or more sets of instructions to remedy the improved blood sugar ofuser 202 via an alimentary solution such as increased consumption ofgrapefruits, configured to be executed by a physical performanceinstruction set based on an updated Comprehensive instruction set 206and/or by following alimentary instruction set 124 in lieu of thatportion of comprehensive instruction set 206. As a further example, asupplement initially presented in Comprehensive instruction set 206 maybe subsequently replaced, in alimentary instruction set 124, by aspecific food categorized as alimentary in order to remedy the issues inwhich the initial supplement sought to address. In another example,alimentary data and alimentary solutions may be incorporated intoalimentary instruction set 124 upon one or more determinations that thealimentary data and implementations of the alimentary solution are moreefficient than non-alimentary solutions initially included in alimentaryinstruction set 124. Alimentary data and alimentary solutions may alsobe substituted for less efficient alimentary solutions. For example, ifuser 202, based on comprehensive instruction set 206, is deemed to needa boost in HDL, then a secondary alimentary solution of eating certainfoods may be determined more efficient than a primary alimentarysolution of increasing cardio activity.

Still referring to FIG. 18, alimentary instruction set generator module120 may generate alimentary instruction set 124, at least in part, byidentifying at least a negative effect associated with an ameliorativeinstruction of comprehensive instruction set 206; at least a negativeeffect may include a “side-effect” of an ameliorative process, such as aside effect of a medication, an increase risk of a type of injuryassociated with an exercise program, or the like. Alimentary instructionset generator module 120 may determine an alimentary instruction thatalleviates the at least a negative effect; for instance, a side-effectof a medication may be alleviated and/or prevented by consumption of analimentary element tending to alleviate the side-effect. As anon-limiting example, a medication that may cause fluid retention andedema may be provided in comprehensive instruction set 206; alimentaryinstruction set generator module 120 may determine that consumption ofan alimentary element having a diuretic effect, such as a food or drinkcontaining caffeine, may act to prevent or alleviate fluid retention. Asa further non-limiting example, Comprehensive instruction set 206 mayinclude an instruction for a user to increase his or her exerciseregimen, or to begin a new regimen of regular exercise; acounterindication and/or other element of data may indicate an elevatedrisk of joint injury and/or inflammation as a result of the increasedexercise, which may be alleviated or prevented by a lower-calorie diet,consumption of foods containing glucosamine or some other ingredientassociated with a reduced risk of joint pain.

Continuing to refer to FIG. 18, alimentary instruction set generatormodule 120 may determine an alimentary instruction that alleviates theat least a negative effect using machine-learning processes and/ormodules as described above; for instance, and without limitation,alimentary instruction set generator module 120 may provide at least anegative effect to ameliorative process label learner and/or alimentaryinstruction set label leaner in the form of at least a prognostic label;ameliorative process label learner and/or alimentary instruction setlabel leaner may generate one or more ameliorative labels associatedwith an alimentary process for alleviating the at least a negativeeffect.

Continuing to refer to FIG. 18, label synthesizer 1204 may groupalimentary labels according to one or more classification systemsrelating the alimentary labels to each other. For instance, plangeneration module 112 and/or label synthesizer 1204 may be configured todetermine that a first alimentary label of the at least an alimentarylabel and a second alimentary label of the at least an alimentary labelbelong to a shared category. A shared category may be a category ofalimentary elements to which each of first alimentary label and secondalimentary label belongs; for instance, a first alimentary labelassociated with tofu and a second alimentary label associated with nutsmay each be grouped as a protein source. A given ameliorative label maybelong to a plurality of overlapping categories. Plan generation module112 may be configured to add a category label associated with a sharedcategory to alimentary instruction set 124, where addition of the labelmay include addition of the label and/or a datum linked to the label,such as a textual or narrative description. In an embodiment,relationships between alimentary labels and categories may be retrievedfrom an alimentary instruction label classification database 2100, forinstance by generating a query using one or more alimentary labels of atleast an alimentary output, entering the query, and receiving one ormore categories matching the query from the alimentary instruction labelclassification database 2100.

Referring now to FIG. 19, an exemplary embodiment of an alimentaryinstruction label classification database 1800 is illustrated.Alimentary instruction label classification database 1800 may operate onthe diagnostic engine 108. Alimentary instruction label classificationdatabase 1800 may be implemented as any database and/or datastoresuitable for use as biological extraction database 400 as describedabove. One or more database tables in alimentary instruction labelclassification database 1800 may include, without limitation, anintervention category table 2100; an intervention may relate eachalimentary label to one or more categories of conditions to be addressedby an alimentary instruction associated with that alimentary label, suchas a nutritional imbalance to be corrected or the like. One or moredatabase tables in alimentary instruction label classification database1800 may include, without limitation, an alimentary category table 2004;which may associate an alimentary instruction label with one or morecategories of nutritional properties, foodstuffs, or the like. One ormore database tables in alimentary instruction label classificationdatabase 1800 may include, without limitation, an action category table1908, which may describe one or more categories of actions, such ascalorie reduction, sugar intake reduction, or the like, to which a givenalimentary instruction may belong. One or more database tables inalimentary instruction label classification database 1800 may include,without limitation, a supplement table 1912, which may describe asupplement that relates to a nutritional need filled by an alimentaryinstruction.

In one embodiment, alimentary instruction set generator module 120 maygenerate alimentary instruction set 208 based on the integration of dataassociated with comprehensive instruction set 206, any applicableexternal sources, and any applicable database within system 100.Generation of alimentary instruction set 124 may include identificationof one or more alimentary instructions in comprehensive instruction set,and insertion of the one or more alimentary instructions in thealimentary instruction set 124; for instance, alimentary instruction set124 may be formed, wholly or partially, by aggregating alimentaryinstructions from comprehensive instruction set 206 and combining theaggregated alimentary instructions using narrative language module,narrative language database, image database, or the like, according toany process suitable for generation of comprehensive instruction set asdescribed above.

In one embodiment, alimentary instruction set generator module 120 maygenerate alimentary instruction set 124 based on alimentary data andnon-alimentary data in order to facilitate both medicinal and holisticcomponents in alimentary instruction set 124 specific to user 202. Inone embodiment, alimentary data may be identified and aggregated into asubset of applicable alimentary data based on at least a biologicalextraction 204 and Comprehensive instruction set 206. In application,alimentary instruction set 124 may comprise a plurality of alimentarydata specific to user 202 that is able to be used by machine learningand artificial intelligence systems in order to continuously update ormodify training sets, and ultimately Comprehensive instruction set 206and alimentary instruction set 124 based on updated or progressionsassociated with implementation of alimentary instruction set 124 by user202. Alimentary data and non-alimentary data may include compilations ofinstruction sets received over a period of time, the compilations mayaccount for improvements or modifications associated with user 202.

In one embodiment, instruction set generation module 120 may identify anon-alimentary instruction within comprehensive instruction set 206,determine an alimentary analog to the non-alimentary instruction andintroduce the alimentary analog into the alimentary instruction setand/or use the alimentary analog to update the physical performanceinstruction set. In one embodiment, non-alimentary data withinalimentary instruction set 124 may be subsequently substituted withalimentary data configured to provide user 202 with holistic solutionsto issues that were initially treated with non-holistic approaches. Forexample, if initially diagnostic output indicates that the blood sugarof user 202 is abnormally high then Comprehensive instruction set 206may recommend that user 202 take applicable medications classified asnon-alimentary in order to lower the blood sugar immediately. Theprocess of user 202 receiving the applicable medications may be based onexecution of one of performances 210-216 by physical performance entity304. However, alimentary instruction set 124 may subsequently provideone or more sets of instructions to remedy the improved blood sugar ofuser 202 via an alimentary solution such as increased consumption ofgrapefruits, configured to be executed by a physical performanceinstruction set based on an updated Comprehensive instruction set 206.As a further example, a supplement initially presented in Comprehensiveinstruction set 206 may be subsequently replaced, in alimentaryinstruction set 124 by a specific food categorized as alimentary inorder to remedy the issues in which the initial supplement sought toaddress. In another example, alimentary data and alimentary solutionsmay be incorporated into alimentary instruction set 124 upon one or moredeterminations that the alimentary data and implementations of thealimentary solution are more efficient than non-alimentary solutionsinitially included in alimentary instruction set 124. Alimentary dataand alimentary solutions may also be substituted for less efficientalimentary solutions. For example, if user 202, based on Comprehensiveinstruction set 206, is deemed to need a boost in HDL, then a secondaryalimentary solution of eating certain foods may be determined moreefficient than a primary alimentary solution of increasing cardioactivity.

In one embodiment, alimentary instruction set generator module 120 maygenerate alimentary instruction set 124, at least in part, byidentifying at least a negative effect associated with an ameliorativeinstruction of Comprehensive instruction set 206; at least a negativeeffect may include a “side-effect” of an ameliorative process, such as aside effect of a medication, an increase risk of a type of injuryassociated with an exercise program, or the like. Alimentary instructionset generator module 120 may determine an alimentary instruction thatalleviates the at least a negative effect; for instance, a side-effectof a medication may be alleviated and/or prevented by consumption of analimentary element tending to alleviate the side-effect. As anon-limiting example, a medication that may cause fluid retention andedema may be provided in Comprehensive instruction set 206; alimentaryinstruction set generator module 120 may determine that consumption ofan alimentary element having a diuretic effect, such as a food or drinkcontaining caffeine, may act to prevent or alleviate fluid retention.

In one embodiment, alimentary instruction set generator module 120 maydetermine an alimentary instruction that alleviates the at least anegative effect using machine-learning processes and/or modules asdescribed above; for instance, and without limitation, alimentaryinstruction set generator module 120 may provide at least a negativeeffect to ameliorative process label learner and/or alimentaryinstruction set label leaner in the form of at least a prognostic label;ameliorative process label learner and/or alimentary instruction setlabel leaner may generate one or more ameliorative labels associatedwith an alimentary process for alleviating the at least a negativeeffect.

In an embodiment, label synthesizer 1204 may be designed and configuredto combine a plurality of labels in at least the alimentary instructionset output together to provide maximally efficient data presentation.Combination of labels together may include elimination of duplicateinformation. For instance, label synthesizer 1204 and/or at least aserver 104 may be designed and configure to determine a first alimentaryinstruction set label of the at least a alimentary instruction set labelis a duplicate of a second alimentary instruction set label of the atleast a alimentary instruction set label and eliminate the firstalimentary instruction set label. Determination that a first alimentaryinstruction set label is a duplicate of a second alimentary instructionset label may include determining that the first alimentary instructionset label is identical to the second alimentary instruction set label;for instance, a alimentary instruction set label generated from testdata presented in one biological extraction of at least a biologicalextraction may be the same as a alimentary instruction set labelgenerated from test data presented in a second biological extraction ofat least a biological extraction. As a further non-limiting example, afirst alimentary instruction set label may be synonymous with a secondalimentary instruction set label, where detection of synonymous labelsmay be performed, without limitation, by a language processing module316 as described above.

In one embodiment, label synthesizer 1204 may group alimentaryinstruction set labels according to one or more classification systemsrelating the alimentary instruction set labels to each other. Forinstance, plan generation module 112 and/or label synthesizer 1204 maybe configured to determine that a first alimentary instruction set labelof the at least a alimentary instruction set label and a secondalimentary instruction set label of the at least a alimentaryinstruction set label belong to a shared category. A shared category maybe a category of conditions or tendencies toward a future condition towhich each of first alimentary instruction set label and secondalimentary instruction set label belongs; as an example, lactoseintolerance and gluten sensitivity may each be examples of digestivesensitivity, for instance, which may in turn share a category with foodsensitivities, food allergies, digestive disorders such as celiacdisease and diverticulitis, or the like. Shared category and/orcategories may be associated with alimentary instruction set labels aswell. A given alimentary instruction set label may belong to a pluralityof overlapping categories. Plan generation module 112 may be configuredto add a category label associated with a shared category toComprehensive instruction set 206, where addition of the label mayinclude addition of the label and/or a datum linked to the label, suchas a textual or narrative description. In an embodiment, relationshipsbetween alimentary instruction set labels and categories may beretrieved from a alimentary instruction set label classificationdatabase 1800, for instance by generating a query using one or morealimentary instruction set labels of at least a alimentary instructionset output, entering the query, and receiving one or more categoriesmatching the query from the alimentary instruction set labelclassification database 1800. In one embodiment, the alimentaryinstruction set label classification database 1800 is configured togenerate queries based on preferences of user 202. Preferences may bebased upon religious, dietary (vegan/gluten-free), lifestyle, or anyother applicable factor associated with user 202 that is able to bemanifested in the alimentary instruction set.

In one embodiment, alimentary instruction set generator module 120 maybe configured to generate alimentary instruction set process descriptor1028 by converting one or more alimentary instruction set labels intonarrative language. As a non-limiting example, nutrition plan generationmodule 120 may include and/or communicate with narrative language unit1312, which may be configured to determine an element of narrativelanguage associated with at least a alimentary instruction set label andinclude the element of narrative language in current alimentaryinstruction set label descriptor. Narrative language unit 1212 mayimplement this, without limitation, by using a language processingmodule 316 to detect one or more associations between alimentaryinstruction set labels, or lists of alimentary instruction set labels,and phrases and/or statements of narrative language. Alternatively oradditionally, Narrative language unit 1212 may retrieve one or moreelements of narrative language from narrative language database 1316,which may contain one or more tables associating alimentary instructionset labels and/or groups of alimentary instruction set labels withwords, sentences, and/or phrases of narrative language. One or moreelements of narrative language may be included in alimentary instructionset, for instance for display to a user as text describing a currentalimentary instruction set status of the user. Alimentary instructionset process descriptor 1228 may further include one or more images; oneor more images may be retrieved by nutrition plan generation module 120from an image database 1120, which may contain one or more tablesassociating alimentary instruction set labels, groups of alimentaryinstruction set labels, alimentary instruction set process descriptors1028, or the like with one or more images.

Referring again to FIG. 1, vibrant constitutional network system mayinclude a client-interface module 128. Client-interface module 128 mayinclude any suitable hardware or software module. Client-interfacemodule 128 is designed and configured to transmit Comprehensiveinstruction set 206 to at least a user client device 132 associated withthe user. A user client device 132 may include, without limitation, adisplay in communication with diagnostic engine 108; display may includeany display as described below in reference to FIG. 22. A user clientdevice 132 may include an addition computing device, such as a mobiledevice, laptop, desktop computer, or the like; as a non-limitingexample, the user client device 132 may be a computer and/or workstationoperated by a medical professional. Output may be displayed on at leasta user client device 132 using an output graphical user interface;output graphical user interface may display at least a currentprognostic descriptor 1000, at least a future prognostic descriptor1024, and/or at least a alimentary instruction set process descriptor1028.

With continued reference to FIG. 1, vibrant constitutional networksystem may include at least an advisory module 132 executing on the atleast a server 104. At least an advisory module may include any suitablehardware or software module. In an embodiment, at least an advisorymodule is designed and configured to generate at least an advisoryoutput as a function of the Comprehensive instruction set 206 and/oralimentary instruction set 124 and transmit the advisory output to atleast an advisor client device 140. At least an advisor client device140 may include any device suitable for use as a user client device 132as described above. At least an advisor client device 140 may be a userclient device 132 as described above; that is, at least an advisoryoutput may be output to the user client device 132. Alternatively oradditionally, at least an advisor client device 140 may be operated byan informed advisor, defined for the purposes of this disclosure as anyperson besides the user who has access to information useable to aiduser in interaction with vibrant constitutional network system. Aninformed advisor may include, without limitation, a medical professionalsuch as a doctor, nurse, nurse practitioner, functional medicinepractitioner, any professional with a career in medicine, nutrition,genetics, fitness, life sciences, insurance, and/or any other applicableindustry that may contribute information and data to system 100regarding medical needs. An informed advisor may include a spiritual orphilosophical advisor, such as a religious leader, pastor, imam, rabbi,or the like. An informed advisor may include a physical fitness advisor,such as without limitation a personal trainer, instructor in yoga ormartial arts, sports coach, or the like.

Referring now to FIG. 20, an exemplary embodiment of an advisory module132 is illustrated. Advisory module 132 may be configured to generate anadvisor instruction set 1800 as a function of the diagnostic output.Advisory instruction set 2000 may contain any element suitable forinclusion in Comprehensive instruction set 206; advisory module 132;Advisory instruction set 2000 and/or any element thereof may begenerated using any process suitable for generation of Comprehensiveinstruction set 206. Advisory instruction set 2000 may include one ormore specialized instructions 2304; specialized instructions, as usedherein, are instructions the contents of which are selected for displayto a particular informed advisor. Selection of instructions for aparticular informed advisor may be obtained, without limitation, frominformation concerning the particular informed advisor, which may beretrieved from a user database 1236 or the like. As a non-limitingexample, where an informed advisor is a doctor, specialized instruction2304 may include data from biological extraction as described above;specialized instruction may include one or more medical records of user,which may, as a non-limiting example, be downloaded or otherwisereceived from an external database containing medical records and/or adatabase (not shown) operating on at least a server 104. As a furthernon-limiting example medical data relevant to fitness, such asorthopedic reports, may be provided to an informed advisor whose role isas a fitness instructor, coach, or the like. Information provided toinformed advisors may be extracted or received from any databasedescribed herein, including without limitation biological extractiondatabase 400.

In an embodiment, and continuing to refer to FIG. 20, advisory module132 may be configured to receive at least an advisory input from theadvisor client device 140. At least an advisory input may include anyinformation provided by an informed advisor via advisor client device140. Advisory input may include medical information and/or advice.Advisory input may include user data, including user habits,preferences, religious affiliations, constitutional restrictions, or thelike. Advisory input may include spiritual and/or religious advice.Advisory input may include user-specific diagnostic information.Advisory input may be provided to user client device 132; alternativelyor additionally, advisory input may be fed back into system 100,including without limitation insertion into user database 1236,inclusion in or use to update diagnostic engine 108, for instance byaugmenting machine-learning models and/or modifying machine-learningoutputs via a lazy-learning protocol or the like as described above.

With continued reference to FIG. 20, advisory module 132 may include anartificial intelligence advisor 2008 configured to perform a usertextual conversation with the user client device 132. Artificialintelligence advisor 2008 may provide output to advisor client device140 and/or user client device 132. Artificial intelligence advisor 2008may receive inputs from advisor client device 140 and/or user clientdevice 132. Inputs and/or outputs may be exchanged using messagingperformances and/or protocols, including without limitation any instantmessaging protocols. Persons skilled in the art, up reviewing theentirety of this disclosure, will be aware of a multiplicity ofcommunication protocols that may be employed to exchange text messagesas described herein. Text messages may be provided in textual formand/or as audio files using, without limitation, speech-to-text and/ortext-to-speech algorithms.

Referring now to FIG. 21, an exemplary embodiment of an artificialintelligence advisor 2008 is illustrated. Artificial intelligenceadvisor 2008 may include a user communication learner 2100. Usercommunication learner 2100 may be any form of machine-learning learneras described above, implementing any form of language processing and/ormachine learning. In an embodiment, user communication learner 2100 mayinclude a general learner 2104; general learner 2104 may be a learnerthat derives relationships between user inputs and correct outputs usinga training set that includes, without limitation, a corpus of previousconversations. Corpus of previous conversations may be logged by atleast a server 104 as conversations take place; user feedback, and/orone or more functions indicating degree of success of a conversation maybe used to differentiate between positive input-output pairs to use fortraining and negative input-output pairs not to use for training.Outputs may include textual strings and/or outputs from any databases,modules, and/or learners as described in this disclosure, includingwithout limitation prognostic labels, prognostic descriptors, alimentaryinstruction set labels, alimentary instruction set descriptors, userinformation, or the like; for instance, general learner 2104 maydetermine that some inputs optimally map to textual response outputs,while other inputs map to outputs created by retrieval of module and/ordatabase outputs, such as retrieval of prognostic descriptors,alimentary instruction set descriptors, or the like. User communicationlearner may include a user-specific learner 2108, which may generate oneor more modules that learn input-output pairs pertaining tocommunication with a particular user; a user specific learner 2108 mayinitially use input-output pairs established by general learner 2104 andmay modify such pairs to match optimal conversation with the particularuser by iteratively minimizing an error function.

Still referring to FIG. 21, general learner 2104 and/or user-specificlearner 2108 may initialize, prior to training, using one or more recordretrieved from a default response database 2112. Default responsedatabase 2112 may link inputs to outputs according to initialrelationships entered by users, including without limitation experts asdescribed above, and/or as created by a previous instance or version ofgeneral learner 2104 and/or user-specific learner 2108. Default responsedatabase 2112 may periodically be updated with information from newlygenerated instances of general learner 2104 and/or user-specific learner2108. Inputs received by artificial intelligence advisor 2008 may bemapped to canonical and/or representative inputs by synonym detection asperformed, for instance, by a language processing module 416; languageprocessing module 316 may be involved in textual analysis and/orgeneration of text at any other point in machine-learning and/orcommunication processes undergone by artificial intelligence advisor2008.

Referring now to FIG. 22, an exemplary embodiment of a default responsedatabase 2112 is illustrated. Default response database 2112 may beimplemented as any database and/or datastore suitable for use asbiological extraction database 400 as described above. One or moredatabase tables in default response database 2112 may include, withoutlimitation, an input/output table 2000, which may link default inputs todefault outputs. Default response database 2112 may include a user table2204, which may, for instance, map users and/or a user client device 132to particular user-specific learners and/or past conversations. Defaultresponse database 2112 may include a user preference table 2208 listingpreferred modes of address, turns of phrase, or other user-specificcommunication preferences. Default response database 2112 may include ageneral preference table 2012, which may track, for instance,output-input pairings associated with greater degrees of usersatisfaction.

Referring again to FIG. 21, artificial intelligence advisor 2008 mayinclude a consultation initiator 2116 configured to detect aconsultation event in a user textual conversation and initiate aconsultation with an informed advisor as a function of the consultationevent. A consultation event, as used herein, is a situation where aninformed advisor is needed to address a user's situation or concerns,such as when a user should be consulting with a nutritionist ordietician seeking to assist user 202 with support pertaining to diet,lifestyle, and wellness, or with an advisor who can lend emotionalsupport when particularly distraught. Detection may be performed,without limitation, by matching an input and/or set of inputs to anoutput that constitutes an action of initiating a consultation; such apairing of an input and/or input set may be learned using a machinelearning process, for instance via general learner and/or user specificlearner 2108. In the latter case, information concerning a particularuser's physical or emotional needs or condition may be a part of thetraining set used to generate the input/input set to consultation eventpairing; for instance, a user with a history of heart disease maytrigger consultation events upon any inputs describing shortness ofbreath, chest discomfort, arrhythmia, or the like. Initiation ofconsultation may include transmitting a message to an advisor clientdevice 140 associated with an appropriate informed advisor, such aswithout limitation transmission of information regarding a potentialmedical emergency to a doctor able to assist in treating the emergency.Initiation of consultation may alternatively or additionally includeproviding an output to the user informing the user that a consultationwith an informed advisor, who may be specified by name or role, isadvisable.

Referring now to FIG. 23, a first exemplary embodiment of a method 2300of generating an alimentary instruction set 124 is illustrated. At step2305, a diagnostic engine operating on at least a server receives atleast a biological extraction from a user; this may be implemented,without limitation, as described above in reference to FIGS. 1-22. Atstep 2310, a diagnostic engine generates a diagnostic output based onthe at least a biological extraction; this may be implemented, withoutlimitation, as described above in reference to FIGS. 1-22. At step 2315,a plan generation module operating on the at least a server generates,based on the diagnostic output, a comprehensive instruction setassociated with the user; this may be implemented, without limitation,as described above in reference to FIGS. 1-22. At step 2320, analimentary instruction set generator module generates, based on thecomprehensive instruction set, an alimentary instruction set associatedwith the user; this may be implemented, without limitation, as describedabove in reference to FIGS. 1-22.

Systems and methods described herein may provide improvements to theprocessing, storage, and utility of data collected along with acentralized vibrant constitutional advice network configured to developcomprehensive plans for users, and execute processes and performancesbased on components of the comprehensive plans. By using a rule-basedmodel or a machine-learned model to generate feature values of datacontained within the collected data, one or more analyses are performedon the feature values, and outputs of training data are generated andincluded in an optimized set of data. The optimized set of data is usedto generate the comprehensive plans, and the vibrant constitutionaladvice network is able to provide users with not only a method ofacquiring detailed genetic and physiological information, but moreimportantly the ability to make decisions that support vibrant healthand longevity influenced by the plurality of information based on thecollected data. Furthermore, the systems and methods provide anunconventional use of the plurality of collected data via automaticexecution of processes and performances by the vibrant constitutionaladvice network based on the generated comprehensive plans. Thus, thesystems and methods described herein improve the functioning ofcomputing systems by optimizing big data processing and improving theutility of the processed big data via its unconventional application,but most importantly the system and methods improve overall health andlifestyle via the centralized platform promoting vibrant life andlongevity.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 24 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 2400 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 2400 includes a processor 2404 and a memory2408 that communicate with each other, and with other components, via abus 2412. Bus 2412 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Memory 2408 may include various components (e.g., machine-readablemedia) including, but not limited to, a random-access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 2416 (BIOS), including basic routines thathelp to transfer information between elements within computer system2400, such as during start-up, may be stored in memory 2408. Memory 2408may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 2420 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 2408 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 2400 may also include a storage device 2424. Examples ofa storage device (e.g., storage device 2424) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 2424 may beconnected to bus 2412 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device2424 (or one or more components thereof) may be removably interfacedwith computer system 2400 (e.g., via an external port connector (notshown)). Particularly, storage device 2424 and an associatedmachine-readable medium 2428 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 2400. In one example,software 2420 may reside, completely or partially, withinmachine-readable medium 2428. In another example, software 2420 mayreside, completely or partially, within processor 2404.

Computer system 2400 may also include an input device 2432. In oneexample, a user of computer system 2400 may enter commands and/or otherinformation into computer system 2400 via input device 2432. Examples ofan input device 2432 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 2432may be interfaced to bus 2412 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 2412, and any combinations thereof. Input device 2432may include a touch screen interface that may be a part of or separatefrom display 2436, discussed further below. Input device 2432 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 2400 via storage device 2424 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 2440. A networkinterface device, such as network interface device 2440, may be utilizedfor connecting computer system 2400 to one or more of a variety ofnetworks, such as network 2444, and one or more remote devices 2448connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 2444, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 2420, etc.) may be communicated to and/or fromcomputer system 2400 via network interface device 2440.

Computer system 2400 may further include a video display adapter 2452for communicating a displayable image to a display device, such asdisplay device 2436. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 2452 and display device 2436 maybe utilized in combination with processor 2404 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 2400 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 2412 via a peripheral interface 2456.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,and systems according to the present disclosure. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

1. A method for generating an alimentary instruction set identifying alist of supplements, comprising: receiving, by a diagnostic engineoperating on a computing device, information related to a biologicalextraction and physiological state of a user; generating, by thediagnostic engine operating on the computing device, a diagnostic outputbased upon the information related to the biological extraction andphysiological state of the user, wherein the generating comprises:identifying, by a machine learning module operating on the computingdevice, a condition of the user as a function of the information relatedto the biological extraction and physiological state of the user and afirst training set, said first training set including a plurality ofdata entries, each first data entry of the plurality of data entriesincluding an element of physiological state data and a correlated firstprognostic label; and identifying, by the machine learning moduleoperating on the computing device, a supplement related to theidentified condition of the user as a function of the identifiedcondition of the user and a second training set, said second trainingset including a plurality of second data entries, each second data entryincluding a second prognostic label and a correlated ameliorativeprocess label; and generating, by an alimentary instruction setgenerator operating on a computing device, a supplement plan as afunction of the diagnostic output, said supplement plan including thesupplement related to the identified condition of the user.
 2. Themethod of claim 1, further comprising: transmitting, by the computingdevice, a physical performance instruction set to a server associatedwith a physical performance entity.
 3. The method of claim 2, whereinthe physical performance instruction set identifies the supplement planthe and wherein the physical performance entity is configured to deliversaid supplement plan to the user.
 4. The method of claim 1, furthercomprising: transmitting, by the alimentary instruction set generatoroperating on the computing device, data related to the supplement planto a client device associated with the user.
 5. The method of claim 4,wherein the data related to the supplement plan is configured to rendera visual representation of the supplement plan in a graphical userinterface on the client device associated with the user.
 6. The methodof claim 1, further comprising: receiving, by the diagnostic engineoperating on the computing device, a dietary preference of the user,wherein the supplement plan is generated further as a function of thedietary preference of the user.
 7. The method of claim 1, wherein thebiological extraction comprises a physical extraction from the user. 8.The method of claim 1, further comprising: generating, via a plangeneration module operating on the computing device, a comprehensiveinstruction set as a function of the diagnostic output, saidcomprehensive instruction set identifying the user's current prognosticstatus.
 9. The method of claim 8, wherein the comprehensive instructionset is generated further as a function of the information related to thebiological extraction and physiological state of the user.
 10. Themethod of claim 8, wherein the supplement plan is generated further as afunction of the comprehensive instruction set.
 11. A system forgenerating an alimentary instruction set identifying a list ofsupplements, comprising: a computing device; a diagnostic engineoperating on the computing device, wherein the diagnostic engine isconfigured to: receive information related to a biological extractionand physiological state of a user; generate a diagnostic output basedupon the information related to the biological extraction andphysiological state of the user, wherein the generating comprises:identifying, by the machine learning module operating on the computingdevice, a condition of the user as a function of the information relatedto the biological extraction and physiological state of the user and afirst training set, said first training set including a plurality ofdata entries, each first data entry of the plurality of data entriesincluding an element of physiological state data and a correlated firstprognostic label; and identifying, by the machine learning moduleoperating on the computing device, a supplement related to theidentified condition of the user as a function of the identifiedcondition of the user and a second training set, said second trainingset including a plurality of second data entries, each second data entryincluding a second prognostic label and a correlated ameliorativeprocess label; and an alimentary instruction set generator operating ona computing device, wherein the alimentary instruction set is configuredto: generate a supplement plan as a function of the diagnostic output,said supplement plan including the supplement related to the identifiedcondition of the user.
 12. The system of claim 11, wherein the computingdevice is configured to: transmit a physical performance instruction setto a server associated with a physical performance entity.
 13. Thesystem of claim 12, wherein the physical performance instruction setidentifies the supplement plan and wherein the physical performanceentity is configured to deliver the supplement plan to the user.
 14. Thesystem of claim 11, wherein the alimentary instruction set generator isfurther configured to: transmit data related to the supplement plan to aclient device associated with the user.
 15. The system of claim 14,wherein the data related to the supplement plan is configured to rendera visual representation of the supplement plan in a graphical userinterface on the client device associated with the user.
 16. The systemof claim 11, wherein the diagnostic engine is further configured toreceive a dietary preference of the user and wherein the alimentaryinstruction set generator is further configured to generate thesupplement plan as a function of the dietary preference of the user. 17.The system of claim 11, wherein the biological extraction comprises aphysical extraction from the user.
 18. The system of claim 11, furthercomprising a plan generation module operating on the computing device,the plan generation module configured to: generate a comprehensiveinstruction set as a function of the diagnostic output, saidcomprehensive instruction set identifying the user's current prognosticstatus.
 19. The system of claim 18, wherein the plan generation moduleis further configured to generate the comprehensive instruction set as afunction of the information related to the biological extraction andphysiological state of the user.
 20. The system of claim 18, wherein thealimentary instruction set generator is further configured to generatethe supplement plan as a function of the comprehensive instruction set.